Concepts, units, scales and dimensions. Vectors. Kinematics of particles. Particle dynamics, inertia, forces and Newton's laws. Friction. Work and energy, conservation of energy. Momentum, collisions. Systems of particles, center of mass. Rotation of a rigid body. Angular momentum and moment of inertia. Statics. Gravity. Solids and fluids, Bernoulli's equation. Oscillations: Simple, damped and forced. Waves. Sound. Temperature. Ideal gas. Heat and the first law of thermodynamics. Kinetic theory of gases. Entropy and the second law of thermodynamics. Home problems: Once a week the students have to solve homeproblems on the website MasteringPhysics.

Laboratory work: Three exercises, mainly centered on mechanics, where students are trained in handling physical instruments, collecting and inspecting data. Students hand in their lab notebooks for a grade.

Note that the textbook is accessible to students via Canvas free of charge.

The purpose of the course is to prepare students for working in technology-based firms and organizations. The course will give an overview of the management of firms and organizations, the role of engineers and the challenges they face. Students will learn about analysis tools used in decision making, interpret the results, and communicate both orally and in writing.

This is a foundational course in single variable calculus. The prerequisites are high school courses on algebra, trigonometry. derivatives, and integrals. The course aims to create a foundation for understanding of subjects such as natural and physical sciences, engineering, economics, and computer science. Topics of the course include the following:

• Real numbers.
• Limits and continuous functions.
• Differentiable functions, rules for derivatives, derivatives of higher order, applications of differential calculus (extremal value problems, linear approximation).
• Transcendental functions.
• Mean value theorem, theorems of l'Hôpital and Taylor.
• Integration, the definite integral and rules/techniques of integration, primitives, improper integrals.
• Fundamental theorem of calculus.
• Applications of integral calculus: Arc length, area, volume, centroids.
• Ordinary differential equations: First-order separable and homogeneous differential equations, first-order linear equations, second-order linear equations with constant coefficients.
• Sequences and series, convergence tests.
• Power series, Taylor series.

Basics of linear algebra over the reals.  

Subject matter: Systems of linear equations, matrices, Gauss-Jordan reduction.  Vector spaces and their subspaces.  Linearly independent sets, bases and dimension.  Linear maps, range space and nullk space.  The dot product, length and angle measures.  Volumes in higher dimension and the cross product in threedimensional space.  Flats, parametric descriptions and descriptions by equations.  Orthogonal projections and orthonormal bases.  Gram-Schmidt orthogonalization.  Determinants and inverses of matrices.  Eigenvalues, eigenvectors and diagonalization.

The Java programming language will be used to introduce basic practices in computer programming. Practice in programming is scheduled throughout the semester. An emphasis is placed on logical methods for writing program and good documentation. Main ideas related to computers and programming. Classes, objects and methods. Control statements. Strings and arrays, operations and built-in functons. Input and output. Inheritance. Ideas relatied to system design and good practices for program writing. Iteration and recursion. Searching and Sorting.

The goal with this course is to prepare students to approach organisations as sequence of operations.  Organisations will be described as a group of operations that produce value adding work.  Companies will be visited and students will describe their workings as operations sequences.  Methods to describe operations, to analyse operations and to present operation sequences will be introduced and trained.  Students will be introduced to methods of describing, analysing and presenting operations overview.  At course end students should be able to use these methods for both descriptions and analysis and then interpret their results to present both orally and in writing.

The course introduces basic drafting concepts and methods to students.  The aim is to equip the student with the necessary skills needed for creating and reading engineering drawings.  Emphasis is placed developing an understanding of 2D representations of 3D geometries. The student is required to learn the drafting methods and be able to perform them by hand in the final exam.  AutoCAD is used in the course as a drafting tool and students will learn how to use it.  The course is, however, not an AutoCAD course but an engineering drawing course.

Basic concepts in probability and statistics based on univariate calculus. 

Topics: 
Sample space, events, probability, equal probability, independent events, conditional probability, Bayes rule, random variables, distribution, density, joint distribution, independent random variables, condistional distribution, mean, variance, covariance, correlation, law of large numbers, Bernoulli, binomial, Poisson, uniform, exponential and normal random variables. Central limit theorem. Poisson process. Random sample, statistics, the distribution of the sample mean and the sample variance. Point estimate, maximum likelihood estimator, mean square error, bias. Interval estimates and hypotheses testing form normal, binomial and exponential samples. Simple linear regression. Goodness of fit tests, test of independence.

Open and closed sets. Mappings, limits and continuity. Differentiable mappings, partial derivatives and the chain rule. Jacobi matrices. Gradients and directional derivatives. Mixed partial derivatives. Curves. Vector fields and flow. Cylindrical and spherical coordinates. Taylor polynomials. Extreme values and the classification of stationary points. Extreme value problems with constraints. Implicit functions and local inverses. Line integrals, primitive functions and exact differential equations. Double integrals. Improper integrals. Green's theorem. Simply connected domains. Change of variables in double integrals. Multiple integrals. Change of variables in multiple integrals. Surface integrals. Integration of vector fields. The theorems of Stokes and Gauss.

The course will cover various data structures, algorithms and abstract data types. Among the data structures covered are lists, stacks, queues, priority queues, trees, binary trees, binary search trees and heaps along with related algorithms. Various search and sort algorithms will be covered. Algorithms will be analysed for their space and time complexity. There will be small programming assignments in Java using the given data structures and algorithms.

The aim of the course is to prepare students in analysing processes and systems. Description of processes are explained so that process analysis can be performed.  Process discovery and their mapping is explained. Three ways of doing process discovery are explained: fact-based, interview-based and workshop-based.  When processes are mapped they can be analyzed.  The course will go through the qualitative methods of: value-stream-analysis, root-cause-analysis and risk analysis. The quantitative methods of: performance management, flow analysis, queuing theory and simulation will be explained.  The later part of the course will be on systems and system analysis.  System thinking will be explained and how systems can be used to describe the world.  The system analysis methods of causal-loop-diagrams and stocks-and-flows will be explained.  The course will conclude by simulating both processes and systems.

X

The aim of this course is offer insights into the analysis and design of technical systems, i.e. systems that use energy, material and information to fulfill given goals. The following topics will be covered in the course:

1) Simple electrical circuits and their use to measure physical properties, such as position, pressure, temperature, and flow.

2) Simple actuators and their use for movement and control of mechanical systems.

3) The basics of automatic control and the use of feedback.

4) The use of microcontrollers for measurement and control of simple technical systems.

5) The process of designing technical systems, including requirements, analysis, implementation, testing, and improvements.

The course is an introductory course in project management. It introduces key concepts of project management and covers context and selection of projects, project planning, project monitoring, management of project teams, and project closure. Students create and execute project plans in groups. Special emphasis is on using of project management for managing technological innovation in organizations.

Functions of a complex variable. Analytic functions. The exponential function, logarithms and roots. Cauchy's Integral Theorem and Cauchy's Integral Formula. Uniform convergence. Power series. Laurent series. Residue integration method. Application of complex function theory to fluid flows. Ordinary differential equations and systems of ordinary differential equations. Linear differential equations with constant coefficients. Systems of linear differential equations. The matrix exponential function. Various methods for obtaining a particular solution. Green's functions for initial value problems. Flows and the phase plane. Nonlinear systems of ordinary differential equations in the plane, equilibrium points, stability and linear approximations. Series solutions and the method of Frobenius. Use of Laplace transforms in solving differential equations.

Introduction to processes and material and energy balance calculations applied to industrial processes. Analysis of gas behavior, gas-liquid systems, and phase equilibrium. Material balances, including reaction systems and multiple-unit systems. Energy balances, including reaction systems and multiple-unit systems, and combined energy-material balances.

In this course, software engineers and computer scientists take the step from programming-in-the-small (i.e. individual developers creating compact modules that solve clearly defined problems) to programming-in-the-large (i.e. teams of developers building complex systems that satisfy vague customer requirements). To deal with the complexities of such projects, this course introduces key software engineering concepts such as agile and plan-driven software process models, requirements engineering, effort estimation, object-oriented analysis and design, software architecture and test-driven development. These concepts are immediately applied in practice as students team up to develop and integrate component-based systems using the Java programming language.

This course will introduce the student to decision and optimization models in operations research. On completing the course the student will be able to formulate, analyze, and solve mathematical models, which represent real-world problems, and critically interpret their results. The course will cover linear programming and the simplex algorithm, as well as related analytical topics. It will also introduce special types of mathematical models, including transportation, assignment, network, and integer programming models. The student will become familiar with a modeling language for linear programming.

X

The purpose of the course is to train an engineering approach to experiments and experimental thinking. Experiments are designed, carried out, data collected and processed using statistical methods. Finally, it discussed how conclusions can be drawn from data / information when using experiments in for example product design and the design and operation of production systems.

Course material: Linear and non-linear regression analysis. Analysis of Variances (ANOVA). Design of experiments. Statistical quality control. Non-parametric tests that can be used in data processing. Use of statistical programs when solving tasks.

The objective of the course is that students get the skills to:

1.    Understand the main concepts in accounting, cost theory and investment theory.

2.    Be able to use methods of measuring the economic feasibility of technical projects.

3.    Be able to develop computer models to assess the profitability of investments, the value of companies and pricing of bonds

Among topics included are accounting, cost theory, cash flow analysis, investment theory, measures of profitability including net present value and internal rate of return, and the building of profitability models. The course ends with a group assignment where the students exercise the development of computer models for feasibility assessment of projects.

This course will introduce the student to decision and optimization models in operations research. On completing the course the student will be able to formulate, analyze, and solve mathematical models, which represent real-world problems, and critically interpret their results. The course will cover integer programming and modeling with stochastic programming. The student will become familiar with building mathematical models using Python.

The aim of the course is to prepare students for participation in product development and design of production systems. The product development process and its individual components will be covered, with special emphasis on productivity and design of production systems (flow, outsourcing, material flow and storage). Emphasis is placed on the use of process and system analyzes and calculation models in decision-making.

The course focuses on the principles of logistics and supply management and gives a broad introduction to the field. The course is divided into three topics primarily. It covers purchase operations of services and inventory management. This part is followed by looking into transportation and distribution management. Finally, the environmental impacts of logistics is studies and all the three parts put together into a view of sustainability. The course consists of lectures, exercises, game (the Beer Game) and a management simulation game to give hands on experience on logistics management,

Organization and management systems. The systems approach. Quality management, quality concepts. Historical development of quality management. Quality cost. Quality in manufacturing. x, R, p, c and cusum-chart. Statistical quality control. Tests of hypotheses. Acceptance sampling - OC curves. Inspection planning. Quality systems and quality assurance. Quality handbook and organizing for quality. ISO 9001. Total Quality Management, improvement step by step, motivations theories. Quality tools. Practical assignment: Designing a quality system for a company.

Basic aspects of computational intelligence, which is the study of algorithms that improve automatically through experience.

The purpose of the course is for students to gain an understanding into planning in both production and services. Students are to master both theoretical and practical methods used in planning. The production management section deals with the creation of forecast models for demand, how they drive the preparation of production plans (both products and components), how work plans and work order share resources and how action plans are used to balance out variability. Service plans cover service levels, the use of service planning, shift plans and more.

X

An insight into the structure of innovation, product development and marketing and how to use this methodology as a tool of management in industrial companies. Theory and practical methods of innovation, product development and marketing. Training in project management and how to run integrated projects covering those three areas by solving realistic problems.

Simulation techniques and system modelling find application in fields as diverse as physics, chemistry, biology, economics, medicine, computer science, and engineering. The purpose of this course is to introduce fundamental principles and concepts in the general area of systems modelling and simulation. Topics to be covered in this course are discrete event simulation, statistical modelling, and simulation modelling design, experimental design, model testing and interpretation of simulation results. The maximum likelihood estimation of probability distributions base on real data is presented. The course will also introduce the generation of random variates and testing. Fundamental programming of simulation models in C is covered and specialized simulation packages introduced. The students will complete a real world simulation project where the emphasis will be on manufacturing or service systems.

In this course the focus is on the methodologies of lean management where the goal is to maximize customer value. The methods used are continuous improvements (Kaizen) on the supply chain, standardized work and leveling out work load on people and machines (Heijunka). Students will learn how to use lean methodologies to maximize quality, minimize lead time while simultaneously lowering cost. Continuous flow (Just in time) can shorten the production and process time while problems in the process are identified immediately when they occur (Jidoka). Students will learn value stream mapping for both production and service process, root cause analysis along with other Lean tools such as, 5S, Kaizen Blitz, A3, visual management, Kanban and SMED. In the cours examples of companies that have gone through Lean transformation will be studied.

Business intelligence are the strategies and technologies companies use to collect, interpret and utilize data for decision support. This course goes beyond reports and dashboards and demonstrates how artificial intelligence can help us gain insights and recommend action. The course is comprised of five learning modules: 1.) regression and classification where data is segmented accoring to predetermined labels. 2.)  semi- and un-labelled data where items are grouped based on similarity measures. 3.) Process mining. 4.) Natural language processing, and 5.) Data ethics. Within each learning module students prepare for class and work in teams on a business problem, followed by an individual assessment.

Students choose project topics in consultation with faculty members.

An overview of some of the main concepts, techniques and algorithms in machine learning. Supervised learning and unsupervised learning. Data preprocessing and data visualization. Model evaluation and model selection. Linear regression, nearest neighbors, support vector machines, decision trees and ensemble methods. Deep learning. Cluster analysis and the k-means algorithm. The students implement simple algorithms in Python and learn how to use specialized software packages. At the end of the course the students work on a practical machine learning project.

The course focuses on simple and multiple linear regression as well as analysis of variance (ANOVA), analysis of covariance (ANCOVA) and binomial regression. The course is a natural continuation of a typical introductory course in statistics taught in various departments of the university.

We will discuss methods for estimating parameters in linear models, how to construct confidence intervals and test hypotheses for the parameters, which assumptions need to hold for applying the models and what to do when they are not met.

Students will work on projects using the statistical software R.

Basis in building web projects, focusing on the frontend: HTML, CSS and JavaScript. Standards, practices and what it takes to create a good site. Design, layout and working with design documents. Programming in the interpreted programming language JavaScript, working with the browser and tools related to that. HTTP standard introduced. Projects focus on creating web sites that use what has been taught.

The role of the fish industry in the Icelandic economy. Fish as raw material, its composition, physical and chemical properties. Fish stocks, fishing gear, selectivity. Storage methods on board and after landing. Processing methods, production process and processing equipment for cooling, superchilling, freezing, salting, drying, canning and shell process. Energy and mass balance for each step in the process and the whole process. 

Objective:
To participate in the international Formula Student project by designing and bulding an electric race car with the purpose of participating in international competitions amongst universities. Strict requirement must be followed to participate and this will give the students valuable experience in designing and implementing practical solutions to difficult engineering problems, which is the main objective of the project.

Part A is the project preparation, planning and technical design.

Methods of classical automatic control systems. System models represented by transfer functions and state equations, simulation. System time and frequency responses. Properties of feedback control systems, stability, sensitivity, disturbance rejection, error coefficients. Stability analysis, Routh's stability criterion. Analysis and design using root-locus, lead, lag and PID controllers. Analysis and design in the frequency domain, lead, lag and PID compensators. Computer controlled systems, A/D and D/A converters, transformations of continuous controllers to discrete form. Analysis and design of digital control systems.

The objective of the course is to introduce students to the core concepts of marketing theory and marketing management. The course covers the marketing concept, marketing orientation, and marketing management. Analysis of the marketing environment is also discussed along with analysis of consumer behavior, marketing strategy, competition, and competitive advantage. Finally, the course focuses on the practical application of marketing concepts for success.

Students choose project topics in consultation with faculty members.

Continuation of web programming I, backend programming in node.js, writing and connecting to web services and connections to databases. Frontend libraries/frameworks used to setup a frontend project (React, Ember, Vue). Security issues that need to be considered when writing for the web. Projects focus on creating web sites that use what has been taught.

This course is intended to introduce students to Engineering Psychology and Human Factors. Engineering psychology focuses on how psychological research can be applied to the design and use of tools, technology, and man-made environment. In particular, how the understanding of the capabilities and limitations of human performance through research on perception, cognition, and behavior, can inform design with the aim of usability and accessibility. In this course the emphasis will be on how learning and design intersect. That is, how the design of educational settings and materials influence learning and how design must take prior knowledge and training into account, as well as accommodate learning.

The course is organized as a seminar and students are expected to both contribute and participate in the discussion. Student work mainly consists of readings, discussions in class and independent course work.

This course is intended for students in educational sciences, psychology, and engineering.

Fundamental concepts on approximation and error estimates. Solutions of systems of linear and non-linear equations. PLU decomposition. Interpolating polynomials, spline interpolation and regression. Numerical differentiation and integration. Extrapolation. Numerical solutions of initial value problems of systems of ordinary differential equations. Multistep methods. Numerical solutions to boundary value problems for ordinary differential equations.

Grades are given for programning projects and in total they amount to 30% of the final grade. The student has to receive the minimum grade of 5 for both the projects and the final exam.

Mechanical systems and mechatronics system elements. Mechanism, motors, drives, motion converters, sensors and transducers. Signal processing and microprocessor.

In this course students are introduced to the basic concepts and methods for parametric representation of curves such as the Bezier-, Hermite- and NURBS curves.  Students will learn about the methods for representing three-dimensional wireframe-, solid- and surface models.  The course will cover the use of parameters when developing and creating three-dimensional modeling, the creation of assembly drawings using mating operators and how different engineering software solutions can communicate. 

The course provides a good fundamental overview of the available engineering software solutions – their advantages and limitations – and the students will learn about the current trends in their field, e.g. in the analysis, simulation, prototyping and manufacturing.  The current trends will be indroduced through guest lectures, company visits and a mini-seminar where the students write articles and present new and exciting research or new techniques (based on peer-review papers). 

Concurrently with the lectures, students work on an unstructured engineering project where they will engineer and build a working prototype, write the results in a report and present the results.

To equip students with the ability to apply interdisciplinary knowledge to design fish processing lines and transportation processes for fish products.

Course Content includes:

• Processing stages and equipment for fresh fish processing, freezing, salting, drying, fishmeal production, silage production, etc.
• Energy and mass balances.
• Design criteria for fish processing companies.
• Processing machinery, packaging and storage methods, competitiveness, profitability, quality issues, technological development, and more.
• Calculations of energy and mass balances for each unit operation and complete processing lines.
• Storage conditions (light, humidity, temperature) and key factors affecting changes in fish products during storage, transportation, and distribution.
• Steady-state and time-dependent heat transfer, application of Heisler and Mollier diagrams.

To participate in the international Formula Student project by designing and bulding an electric race car with the purpose of participating in international competitions amongst universities. Strict requirement must be followed to participate and this will give the students valuable experience in designing and implementing practical solutions to difficult engineering problems, which is the main objective of the project.

Part B is the construction of the car and preparing of participation in the international student competition.

Good corporate governance and skilled financial management are the key ingredients for a successfully run corporation.  Finance II builds on the course Finance I, and has its main focus on the corporation and how it is being run from financial management point of view.  The course covers topics in corporate governance, how incentives are embedded in the operation of the firm and what economic and financial outcomes are to be expected from the incentive structure.  The main focus of the course is financial management; the firm’s capital structure, short and long term financing, capital budgeting, dividend policies, short term financial planning as well as financial distress.

The learning path starts with overall orientation and definitions. The focus at first is on challenges in the external environment and how organizational effectiveness can be assessed. Then the focus is on understanding and analysing the structure of organizations and on what should be in the picture when an organizational structure is designed. Influences and challenges related to organizational design and ethical and societal challenges are reviewed. Innovation, knowledge, information, and decision making in the context of organizational change are also covered. Then the impact of artificial intelligence on the structure and working methods of organizations will be discussed.

The focus of the course is on the methodology for developing marketing plans, from market analysis to action plans.  Methods for analyzing market position, for creating marketing strategy, and for the selection of marketing tactics are discussed.
Students develop marketing plans for goods or services, working in groups of four.  Students will contact an organization and develop a marketing plan in alliance with that specific organization.
The course format is a mix of lectures, discussions and project work.

The learning path is in line with the structure of the textbook, starts with overall orientation and definitions. Then the focus is on external and internal analysis in order to assess the strategic situation of companies. After that issues of strategy development are covered and that of strategy implementation. The class will use different teaching methods and the students are required to work on cases and examples of real companies.

Concepts, units, scales and dimensions. Vectors. Kinematics of particles. Particle dynamics, inertia, forces and Newton's laws. Friction. Work and energy, conservation of energy. Momentum, collisions. Systems of particles, center of mass. Rotation of a rigid body. Angular momentum and moment of inertia. Statics. Gravity. Solids and fluids, Bernoulli's equation. Oscillations: Simple, damped and forced. Waves. Sound. Temperature. Ideal gas. Heat and the first law of thermodynamics. Kinetic theory of gases. Entropy and the second law of thermodynamics. Home problems: Once a week the students have to solve homeproblems on the website MasteringPhysics.

Laboratory work: Three exercises, mainly centered on mechanics, where students are trained in handling physical instruments, collecting and inspecting data. Students hand in their lab notebooks for a grade.

Note that the textbook is accessible to students via Canvas free of charge.

The purpose of the course is to prepare students for working in technology-based firms and organizations. The course will give an overview of the management of firms and organizations, the role of engineers and the challenges they face. Students will learn about analysis tools used in decision making, interpret the results, and communicate both orally and in writing.

This is a foundational course in single variable calculus. The prerequisites are high school courses on algebra, trigonometry. derivatives, and integrals. The course aims to create a foundation for understanding of subjects such as natural and physical sciences, engineering, economics, and computer science. Topics of the course include the following:

• Real numbers.
• Limits and continuous functions.
• Differentiable functions, rules for derivatives, derivatives of higher order, applications of differential calculus (extremal value problems, linear approximation).
• Transcendental functions.
• Mean value theorem, theorems of l'Hôpital and Taylor.
• Integration, the definite integral and rules/techniques of integration, primitives, improper integrals.
• Fundamental theorem of calculus.
• Applications of integral calculus: Arc length, area, volume, centroids.
• Ordinary differential equations: First-order separable and homogeneous differential equations, first-order linear equations, second-order linear equations with constant coefficients.
• Sequences and series, convergence tests.
• Power series, Taylor series.

Basics of linear algebra over the reals.  

Subject matter: Systems of linear equations, matrices, Gauss-Jordan reduction.  Vector spaces and their subspaces.  Linearly independent sets, bases and dimension.  Linear maps, range space and nullk space.  The dot product, length and angle measures.  Volumes in higher dimension and the cross product in threedimensional space.  Flats, parametric descriptions and descriptions by equations.  Orthogonal projections and orthonormal bases.  Gram-Schmidt orthogonalization.  Determinants and inverses of matrices.  Eigenvalues, eigenvectors and diagonalization.

The Java programming language will be used to introduce basic practices in computer programming. Practice in programming is scheduled throughout the semester. An emphasis is placed on logical methods for writing program and good documentation. Main ideas related to computers and programming. Classes, objects and methods. Control statements. Strings and arrays, operations and built-in functons. Input and output. Inheritance. Ideas relatied to system design and good practices for program writing. Iteration and recursion. Searching and Sorting.

The goal with this course is to prepare students to approach organisations as sequence of operations.  Organisations will be described as a group of operations that produce value adding work.  Companies will be visited and students will describe their workings as operations sequences.  Methods to describe operations, to analyse operations and to present operation sequences will be introduced and trained.  Students will be introduced to methods of describing, analysing and presenting operations overview.  At course end students should be able to use these methods for both descriptions and analysis and then interpret their results to present both orally and in writing.

The course introduces basic drafting concepts and methods to students.  The aim is to equip the student with the necessary skills needed for creating and reading engineering drawings.  Emphasis is placed developing an understanding of 2D representations of 3D geometries. The student is required to learn the drafting methods and be able to perform them by hand in the final exam.  AutoCAD is used in the course as a drafting tool and students will learn how to use it.  The course is, however, not an AutoCAD course but an engineering drawing course.

Basic concepts in probability and statistics based on univariate calculus. 

Topics: 
Sample space, events, probability, equal probability, independent events, conditional probability, Bayes rule, random variables, distribution, density, joint distribution, independent random variables, condistional distribution, mean, variance, covariance, correlation, law of large numbers, Bernoulli, binomial, Poisson, uniform, exponential and normal random variables. Central limit theorem. Poisson process. Random sample, statistics, the distribution of the sample mean and the sample variance. Point estimate, maximum likelihood estimator, mean square error, bias. Interval estimates and hypotheses testing form normal, binomial and exponential samples. Simple linear regression. Goodness of fit tests, test of independence.

Open and closed sets. Mappings, limits and continuity. Differentiable mappings, partial derivatives and the chain rule. Jacobi matrices. Gradients and directional derivatives. Mixed partial derivatives. Curves. Vector fields and flow. Cylindrical and spherical coordinates. Taylor polynomials. Extreme values and the classification of stationary points. Extreme value problems with constraints. Implicit functions and local inverses. Line integrals, primitive functions and exact differential equations. Double integrals. Improper integrals. Green's theorem. Simply connected domains. Change of variables in double integrals. Multiple integrals. Change of variables in multiple integrals. Surface integrals. Integration of vector fields. The theorems of Stokes and Gauss.

The course will cover various data structures, algorithms and abstract data types. Among the data structures covered are lists, stacks, queues, priority queues, trees, binary trees, binary search trees and heaps along with related algorithms. Various search and sort algorithms will be covered. Algorithms will be analysed for their space and time complexity. There will be small programming assignments in Java using the given data structures and algorithms.

The aim of the course is to prepare students in analysing processes and systems. Description of processes are explained so that process analysis can be performed.  Process discovery and their mapping is explained. Three ways of doing process discovery are explained: fact-based, interview-based and workshop-based.  When processes are mapped they can be analyzed.  The course will go through the qualitative methods of: value-stream-analysis, root-cause-analysis and risk analysis. The quantitative methods of: performance management, flow analysis, queuing theory and simulation will be explained.  The later part of the course will be on systems and system analysis.  System thinking will be explained and how systems can be used to describe the world.  The system analysis methods of causal-loop-diagrams and stocks-and-flows will be explained.  The course will conclude by simulating both processes and systems.

X

The aim of this course is offer insights into the analysis and design of technical systems, i.e. systems that use energy, material and information to fulfill given goals. The following topics will be covered in the course:

1) Simple electrical circuits and their use to measure physical properties, such as position, pressure, temperature, and flow.

2) Simple actuators and their use for movement and control of mechanical systems.

3) The basics of automatic control and the use of feedback.

4) The use of microcontrollers for measurement and control of simple technical systems.

5) The process of designing technical systems, including requirements, analysis, implementation, testing, and improvements.

The course is an introductory course in project management. It introduces key concepts of project management and covers context and selection of projects, project planning, project monitoring, management of project teams, and project closure. Students create and execute project plans in groups. Special emphasis is on using of project management for managing technological innovation in organizations.

Functions of a complex variable. Analytic functions. The exponential function, logarithms and roots. Cauchy's Integral Theorem and Cauchy's Integral Formula. Uniform convergence. Power series. Laurent series. Residue integration method. Application of complex function theory to fluid flows. Ordinary differential equations and systems of ordinary differential equations. Linear differential equations with constant coefficients. Systems of linear differential equations. The matrix exponential function. Various methods for obtaining a particular solution. Green's functions for initial value problems. Flows and the phase plane. Nonlinear systems of ordinary differential equations in the plane, equilibrium points, stability and linear approximations. Series solutions and the method of Frobenius. Use of Laplace transforms in solving differential equations.

Introduction to processes and material and energy balance calculations applied to industrial processes. Analysis of gas behavior, gas-liquid systems, and phase equilibrium. Material balances, including reaction systems and multiple-unit systems. Energy balances, including reaction systems and multiple-unit systems, and combined energy-material balances.

In this course, software engineers and computer scientists take the step from programming-in-the-small (i.e. individual developers creating compact modules that solve clearly defined problems) to programming-in-the-large (i.e. teams of developers building complex systems that satisfy vague customer requirements). To deal with the complexities of such projects, this course introduces key software engineering concepts such as agile and plan-driven software process models, requirements engineering, effort estimation, object-oriented analysis and design, software architecture and test-driven development. These concepts are immediately applied in practice as students team up to develop and integrate component-based systems using the Java programming language.

This course will introduce the student to decision and optimization models in operations research. On completing the course the student will be able to formulate, analyze, and solve mathematical models, which represent real-world problems, and critically interpret their results. The course will cover linear programming and the simplex algorithm, as well as related analytical topics. It will also introduce special types of mathematical models, including transportation, assignment, network, and integer programming models. The student will become familiar with a modeling language for linear programming.

X

The purpose of the course is to train an engineering approach to experiments and experimental thinking. Experiments are designed, carried out, data collected and processed using statistical methods. Finally, it discussed how conclusions can be drawn from data / information when using experiments in for example product design and the design and operation of production systems.

Course material: Linear and non-linear regression analysis. Analysis of Variances (ANOVA). Design of experiments. Statistical quality control. Non-parametric tests that can be used in data processing. Use of statistical programs when solving tasks.

The objective of the course is that students get the skills to:

1.    Understand the main concepts in accounting, cost theory and investment theory.

2.    Be able to use methods of measuring the economic feasibility of technical projects.

3.    Be able to develop computer models to assess the profitability of investments, the value of companies and pricing of bonds

Among topics included are accounting, cost theory, cash flow analysis, investment theory, measures of profitability including net present value and internal rate of return, and the building of profitability models. The course ends with a group assignment where the students exercise the development of computer models for feasibility assessment of projects.

This course will introduce the student to decision and optimization models in operations research. On completing the course the student will be able to formulate, analyze, and solve mathematical models, which represent real-world problems, and critically interpret their results. The course will cover integer programming and modeling with stochastic programming. The student will become familiar with building mathematical models using Python.

The aim of the course is to prepare students for participation in product development and design of production systems. The product development process and its individual components will be covered, with special emphasis on productivity and design of production systems (flow, outsourcing, material flow and storage). Emphasis is placed on the use of process and system analyzes and calculation models in decision-making.

The course focuses on the principles of logistics and supply management and gives a broad introduction to the field. The course is divided into three topics primarily. It covers purchase operations of services and inventory management. This part is followed by looking into transportation and distribution management. Finally, the environmental impacts of logistics is studies and all the three parts put together into a view of sustainability. The course consists of lectures, exercises, game (the Beer Game) and a management simulation game to give hands on experience on logistics management,

Organization and management systems. The systems approach. Quality management, quality concepts. Historical development of quality management. Quality cost. Quality in manufacturing. x, R, p, c and cusum-chart. Statistical quality control. Tests of hypotheses. Acceptance sampling - OC curves. Inspection planning. Quality systems and quality assurance. Quality handbook and organizing for quality. ISO 9001. Total Quality Management, improvement step by step, motivations theories. Quality tools. Practical assignment: Designing a quality system for a company.

Basic aspects of computational intelligence, which is the study of algorithms that improve automatically through experience.

The purpose of the course is for students to gain an understanding into planning in both production and services. Students are to master both theoretical and practical methods used in planning. The production management section deals with the creation of forecast models for demand, how they drive the preparation of production plans (both products and components), how work plans and work order share resources and how action plans are used to balance out variability. Service plans cover service levels, the use of service planning, shift plans and more.

X

An insight into the structure of innovation, product development and marketing and how to use this methodology as a tool of management in industrial companies. Theory and practical methods of innovation, product development and marketing. Training in project management and how to run integrated projects covering those three areas by solving realistic problems.

Simulation techniques and system modelling find application in fields as diverse as physics, chemistry, biology, economics, medicine, computer science, and engineering. The purpose of this course is to introduce fundamental principles and concepts in the general area of systems modelling and simulation. Topics to be covered in this course are discrete event simulation, statistical modelling, and simulation modelling design, experimental design, model testing and interpretation of simulation results. The maximum likelihood estimation of probability distributions base on real data is presented. The course will also introduce the generation of random variates and testing. Fundamental programming of simulation models in C is covered and specialized simulation packages introduced. The students will complete a real world simulation project where the emphasis will be on manufacturing or service systems.

In this course the focus is on the methodologies of lean management where the goal is to maximize customer value. The methods used are continuous improvements (Kaizen) on the supply chain, standardized work and leveling out work load on people and machines (Heijunka). Students will learn how to use lean methodologies to maximize quality, minimize lead time while simultaneously lowering cost. Continuous flow (Just in time) can shorten the production and process time while problems in the process are identified immediately when they occur (Jidoka). Students will learn value stream mapping for both production and service process, root cause analysis along with other Lean tools such as, 5S, Kaizen Blitz, A3, visual management, Kanban and SMED. In the cours examples of companies that have gone through Lean transformation will be studied.

Business intelligence are the strategies and technologies companies use to collect, interpret and utilize data for decision support. This course goes beyond reports and dashboards and demonstrates how artificial intelligence can help us gain insights and recommend action. The course is comprised of five learning modules: 1.) regression and classification where data is segmented accoring to predetermined labels. 2.)  semi- and un-labelled data where items are grouped based on similarity measures. 3.) Process mining. 4.) Natural language processing, and 5.) Data ethics. Within each learning module students prepare for class and work in teams on a business problem, followed by an individual assessment.

Students choose project topics in consultation with faculty members.

An overview of some of the main concepts, techniques and algorithms in machine learning. Supervised learning and unsupervised learning. Data preprocessing and data visualization. Model evaluation and model selection. Linear regression, nearest neighbors, support vector machines, decision trees and ensemble methods. Deep learning. Cluster analysis and the k-means algorithm. The students implement simple algorithms in Python and learn how to use specialized software packages. At the end of the course the students work on a practical machine learning project.

The course focuses on simple and multiple linear regression as well as analysis of variance (ANOVA), analysis of covariance (ANCOVA) and binomial regression. The course is a natural continuation of a typical introductory course in statistics taught in various departments of the university.

We will discuss methods for estimating parameters in linear models, how to construct confidence intervals and test hypotheses for the parameters, which assumptions need to hold for applying the models and what to do when they are not met.

Students will work on projects using the statistical software R.

Basis in building web projects, focusing on the frontend: HTML, CSS and JavaScript. Standards, practices and what it takes to create a good site. Design, layout and working with design documents. Programming in the interpreted programming language JavaScript, working with the browser and tools related to that. HTTP standard introduced. Projects focus on creating web sites that use what has been taught.

The role of the fish industry in the Icelandic economy. Fish as raw material, its composition, physical and chemical properties. Fish stocks, fishing gear, selectivity. Storage methods on board and after landing. Processing methods, production process and processing equipment for cooling, superchilling, freezing, salting, drying, canning and shell process. Energy and mass balance for each step in the process and the whole process. 

Objective:
To participate in the international Formula Student project by designing and bulding an electric race car with the purpose of participating in international competitions amongst universities. Strict requirement must be followed to participate and this will give the students valuable experience in designing and implementing practical solutions to difficult engineering problems, which is the main objective of the project.

Part A is the project preparation, planning and technical design.

Methods of classical automatic control systems. System models represented by transfer functions and state equations, simulation. System time and frequency responses. Properties of feedback control systems, stability, sensitivity, disturbance rejection, error coefficients. Stability analysis, Routh's stability criterion. Analysis and design using root-locus, lead, lag and PID controllers. Analysis and design in the frequency domain, lead, lag and PID compensators. Computer controlled systems, A/D and D/A converters, transformations of continuous controllers to discrete form. Analysis and design of digital control systems.

The objective of the course is to introduce students to the core concepts of marketing theory and marketing management. The course covers the marketing concept, marketing orientation, and marketing management. Analysis of the marketing environment is also discussed along with analysis of consumer behavior, marketing strategy, competition, and competitive advantage. Finally, the course focuses on the practical application of marketing concepts for success.

Students choose project topics in consultation with faculty members.

Continuation of web programming I, backend programming in node.js, writing and connecting to web services and connections to databases. Frontend libraries/frameworks used to setup a frontend project (React, Ember, Vue). Security issues that need to be considered when writing for the web. Projects focus on creating web sites that use what has been taught.

This course is intended to introduce students to Engineering Psychology and Human Factors. Engineering psychology focuses on how psychological research can be applied to the design and use of tools, technology, and man-made environment. In particular, how the understanding of the capabilities and limitations of human performance through research on perception, cognition, and behavior, can inform design with the aim of usability and accessibility. In this course the emphasis will be on how learning and design intersect. That is, how the design of educational settings and materials influence learning and how design must take prior knowledge and training into account, as well as accommodate learning.

The course is organized as a seminar and students are expected to both contribute and participate in the discussion. Student work mainly consists of readings, discussions in class and independent course work.

This course is intended for students in educational sciences, psychology, and engineering.

Fundamental concepts on approximation and error estimates. Solutions of systems of linear and non-linear equations. PLU decomposition. Interpolating polynomials, spline interpolation and regression. Numerical differentiation and integration. Extrapolation. Numerical solutions of initial value problems of systems of ordinary differential equations. Multistep methods. Numerical solutions to boundary value problems for ordinary differential equations.

Grades are given for programning projects and in total they amount to 30% of the final grade. The student has to receive the minimum grade of 5 for both the projects and the final exam.

Mechanical systems and mechatronics system elements. Mechanism, motors, drives, motion converters, sensors and transducers. Signal processing and microprocessor.

In this course students are introduced to the basic concepts and methods for parametric representation of curves such as the Bezier-, Hermite- and NURBS curves.  Students will learn about the methods for representing three-dimensional wireframe-, solid- and surface models.  The course will cover the use of parameters when developing and creating three-dimensional modeling, the creation of assembly drawings using mating operators and how different engineering software solutions can communicate. 

The course provides a good fundamental overview of the available engineering software solutions – their advantages and limitations – and the students will learn about the current trends in their field, e.g. in the analysis, simulation, prototyping and manufacturing.  The current trends will be indroduced through guest lectures, company visits and a mini-seminar where the students write articles and present new and exciting research or new techniques (based on peer-review papers). 

Concurrently with the lectures, students work on an unstructured engineering project where they will engineer and build a working prototype, write the results in a report and present the results.

To equip students with the ability to apply interdisciplinary knowledge to design fish processing lines and transportation processes for fish products.

Course Content includes:

• Processing stages and equipment for fresh fish processing, freezing, salting, drying, fishmeal production, silage production, etc.
• Energy and mass balances.
• Design criteria for fish processing companies.
• Processing machinery, packaging and storage methods, competitiveness, profitability, quality issues, technological development, and more.
• Calculations of energy and mass balances for each unit operation and complete processing lines.
• Storage conditions (light, humidity, temperature) and key factors affecting changes in fish products during storage, transportation, and distribution.
• Steady-state and time-dependent heat transfer, application of Heisler and Mollier diagrams.

To participate in the international Formula Student project by designing and bulding an electric race car with the purpose of participating in international competitions amongst universities. Strict requirement must be followed to participate and this will give the students valuable experience in designing and implementing practical solutions to difficult engineering problems, which is the main objective of the project.

Part B is the construction of the car and preparing of participation in the international student competition.

Good corporate governance and skilled financial management are the key ingredients for a successfully run corporation.  Finance II builds on the course Finance I, and has its main focus on the corporation and how it is being run from financial management point of view.  The course covers topics in corporate governance, how incentives are embedded in the operation of the firm and what economic and financial outcomes are to be expected from the incentive structure.  The main focus of the course is financial management; the firm’s capital structure, short and long term financing, capital budgeting, dividend policies, short term financial planning as well as financial distress.

The learning path starts with overall orientation and definitions. The focus at first is on challenges in the external environment and how organizational effectiveness can be assessed. Then the focus is on understanding and analysing the structure of organizations and on what should be in the picture when an organizational structure is designed. Influences and challenges related to organizational design and ethical and societal challenges are reviewed. Innovation, knowledge, information, and decision making in the context of organizational change are also covered. Then the impact of artificial intelligence on the structure and working methods of organizations will be discussed.

The focus of the course is on the methodology for developing marketing plans, from market analysis to action plans.  Methods for analyzing market position, for creating marketing strategy, and for the selection of marketing tactics are discussed.
Students develop marketing plans for goods or services, working in groups of four.  Students will contact an organization and develop a marketing plan in alliance with that specific organization.
The course format is a mix of lectures, discussions and project work.

The learning path is in line with the structure of the textbook, starts with overall orientation and definitions. Then the focus is on external and internal analysis in order to assess the strategic situation of companies. After that issues of strategy development are covered and that of strategy implementation. The class will use different teaching methods and the students are required to work on cases and examples of real companies.

Concepts, units, scales and dimensions. Vectors. Kinematics of particles. Particle dynamics, inertia, forces and Newton's laws. Friction. Work and energy, conservation of energy. Momentum, collisions. Systems of particles, center of mass. Rotation of a rigid body. Angular momentum and moment of inertia. Statics. Gravity. Solids and fluids, Bernoulli's equation. Oscillations: Simple, damped and forced. Waves. Sound. Temperature. Ideal gas. Heat and the first law of thermodynamics. Kinetic theory of gases. Entropy and the second law of thermodynamics. Home problems: Once a week the students have to solve homeproblems on the website MasteringPhysics.

Laboratory work: Three exercises, mainly centered on mechanics, where students are trained in handling physical instruments, collecting and inspecting data. Students hand in their lab notebooks for a grade.

Note that the textbook is accessible to students via Canvas free of charge.

The purpose of the course is to prepare students for working in technology-based firms and organizations. The course will give an overview of the management of firms and organizations, the role of engineers and the challenges they face. Students will learn about analysis tools used in decision making, interpret the results, and communicate both orally and in writing.

This is a foundational course in single variable calculus. The prerequisites are high school courses on algebra, trigonometry. derivatives, and integrals. The course aims to create a foundation for understanding of subjects such as natural and physical sciences, engineering, economics, and computer science. Topics of the course include the following:

• Real numbers.
• Limits and continuous functions.
• Differentiable functions, rules for derivatives, derivatives of higher order, applications of differential calculus (extremal value problems, linear approximation).
• Transcendental functions.
• Mean value theorem, theorems of l'Hôpital and Taylor.
• Integration, the definite integral and rules/techniques of integration, primitives, improper integrals.
• Fundamental theorem of calculus.
• Applications of integral calculus: Arc length, area, volume, centroids.
• Ordinary differential equations: First-order separable and homogeneous differential equations, first-order linear equations, second-order linear equations with constant coefficients.
• Sequences and series, convergence tests.
• Power series, Taylor series.

Basics of linear algebra over the reals.  

Subject matter: Systems of linear equations, matrices, Gauss-Jordan reduction.  Vector spaces and their subspaces.  Linearly independent sets, bases and dimension.  Linear maps, range space and nullk space.  The dot product, length and angle measures.  Volumes in higher dimension and the cross product in threedimensional space.  Flats, parametric descriptions and descriptions by equations.  Orthogonal projections and orthonormal bases.  Gram-Schmidt orthogonalization.  Determinants and inverses of matrices.  Eigenvalues, eigenvectors and diagonalization.

The Java programming language will be used to introduce basic practices in computer programming. Practice in programming is scheduled throughout the semester. An emphasis is placed on logical methods for writing program and good documentation. Main ideas related to computers and programming. Classes, objects and methods. Control statements. Strings and arrays, operations and built-in functons. Input and output. Inheritance. Ideas relatied to system design and good practices for program writing. Iteration and recursion. Searching and Sorting.

The goal with this course is to prepare students to approach organisations as sequence of operations.  Organisations will be described as a group of operations that produce value adding work.  Companies will be visited and students will describe their workings as operations sequences.  Methods to describe operations, to analyse operations and to present operation sequences will be introduced and trained.  Students will be introduced to methods of describing, analysing and presenting operations overview.  At course end students should be able to use these methods for both descriptions and analysis and then interpret their results to present both orally and in writing.

The course introduces basic drafting concepts and methods to students.  The aim is to equip the student with the necessary skills needed for creating and reading engineering drawings.  Emphasis is placed developing an understanding of 2D representations of 3D geometries. The student is required to learn the drafting methods and be able to perform them by hand in the final exam.  AutoCAD is used in the course as a drafting tool and students will learn how to use it.  The course is, however, not an AutoCAD course but an engineering drawing course.

Basic concepts in probability and statistics based on univariate calculus. 

Topics: 
Sample space, events, probability, equal probability, independent events, conditional probability, Bayes rule, random variables, distribution, density, joint distribution, independent random variables, condistional distribution, mean, variance, covariance, correlation, law of large numbers, Bernoulli, binomial, Poisson, uniform, exponential and normal random variables. Central limit theorem. Poisson process. Random sample, statistics, the distribution of the sample mean and the sample variance. Point estimate, maximum likelihood estimator, mean square error, bias. Interval estimates and hypotheses testing form normal, binomial and exponential samples. Simple linear regression. Goodness of fit tests, test of independence.

Open and closed sets. Mappings, limits and continuity. Differentiable mappings, partial derivatives and the chain rule. Jacobi matrices. Gradients and directional derivatives. Mixed partial derivatives. Curves. Vector fields and flow. Cylindrical and spherical coordinates. Taylor polynomials. Extreme values and the classification of stationary points. Extreme value problems with constraints. Implicit functions and local inverses. Line integrals, primitive functions and exact differential equations. Double integrals. Improper integrals. Green's theorem. Simply connected domains. Change of variables in double integrals. Multiple integrals. Change of variables in multiple integrals. Surface integrals. Integration of vector fields. The theorems of Stokes and Gauss.

The course will cover various data structures, algorithms and abstract data types. Among the data structures covered are lists, stacks, queues, priority queues, trees, binary trees, binary search trees and heaps along with related algorithms. Various search and sort algorithms will be covered. Algorithms will be analysed for their space and time complexity. There will be small programming assignments in Java using the given data structures and algorithms.

The aim of the course is to prepare students in analysing processes and systems. Description of processes are explained so that process analysis can be performed.  Process discovery and their mapping is explained. Three ways of doing process discovery are explained: fact-based, interview-based and workshop-based.  When processes are mapped they can be analyzed.  The course will go through the qualitative methods of: value-stream-analysis, root-cause-analysis and risk analysis. The quantitative methods of: performance management, flow analysis, queuing theory and simulation will be explained.  The later part of the course will be on systems and system analysis.  System thinking will be explained and how systems can be used to describe the world.  The system analysis methods of causal-loop-diagrams and stocks-and-flows will be explained.  The course will conclude by simulating both processes and systems.

X

The aim of this course is offer insights into the analysis and design of technical systems, i.e. systems that use energy, material and information to fulfill given goals. The following topics will be covered in the course:

1) Simple electrical circuits and their use to measure physical properties, such as position, pressure, temperature, and flow.

2) Simple actuators and their use for movement and control of mechanical systems.

3) The basics of automatic control and the use of feedback.

4) The use of microcontrollers for measurement and control of simple technical systems.

5) The process of designing technical systems, including requirements, analysis, implementation, testing, and improvements.

The course is an introductory course in project management. It introduces key concepts of project management and covers context and selection of projects, project planning, project monitoring, management of project teams, and project closure. Students create and execute project plans in groups. Special emphasis is on using of project management for managing technological innovation in organizations.

Functions of a complex variable. Analytic functions. The exponential function, logarithms and roots. Cauchy's Integral Theorem and Cauchy's Integral Formula. Uniform convergence. Power series. Laurent series. Residue integration method. Application of complex function theory to fluid flows. Ordinary differential equations and systems of ordinary differential equations. Linear differential equations with constant coefficients. Systems of linear differential equations. The matrix exponential function. Various methods for obtaining a particular solution. Green's functions for initial value problems. Flows and the phase plane. Nonlinear systems of ordinary differential equations in the plane, equilibrium points, stability and linear approximations. Series solutions and the method of Frobenius. Use of Laplace transforms in solving differential equations.

Introduction to processes and material and energy balance calculations applied to industrial processes. Analysis of gas behavior, gas-liquid systems, and phase equilibrium. Material balances, including reaction systems and multiple-unit systems. Energy balances, including reaction systems and multiple-unit systems, and combined energy-material balances.

In this course, software engineers and computer scientists take the step from programming-in-the-small (i.e. individual developers creating compact modules that solve clearly defined problems) to programming-in-the-large (i.e. teams of developers building complex systems that satisfy vague customer requirements). To deal with the complexities of such projects, this course introduces key software engineering concepts such as agile and plan-driven software process models, requirements engineering, effort estimation, object-oriented analysis and design, software architecture and test-driven development. These concepts are immediately applied in practice as students team up to develop and integrate component-based systems using the Java programming language.

This course will introduce the student to decision and optimization models in operations research. On completing the course the student will be able to formulate, analyze, and solve mathematical models, which represent real-world problems, and critically interpret their results. The course will cover linear programming and the simplex algorithm, as well as related analytical topics. It will also introduce special types of mathematical models, including transportation, assignment, network, and integer programming models. The student will become familiar with a modeling language for linear programming.

X

The purpose of the course is to train an engineering approach to experiments and experimental thinking. Experiments are designed, carried out, data collected and processed using statistical methods. Finally, it discussed how conclusions can be drawn from data / information when using experiments in for example product design and the design and operation of production systems.

Course material: Linear and non-linear regression analysis. Analysis of Variances (ANOVA). Design of experiments. Statistical quality control. Non-parametric tests that can be used in data processing. Use of statistical programs when solving tasks.

The objective of the course is that students get the skills to:

1.    Understand the main concepts in accounting, cost theory and investment theory.

2.    Be able to use methods of measuring the economic feasibility of technical projects.

3.    Be able to develop computer models to assess the profitability of investments, the value of companies and pricing of bonds

Among topics included are accounting, cost theory, cash flow analysis, investment theory, measures of profitability including net present value and internal rate of return, and the building of profitability models. The course ends with a group assignment where the students exercise the development of computer models for feasibility assessment of projects.

This course will introduce the student to decision and optimization models in operations research. On completing the course the student will be able to formulate, analyze, and solve mathematical models, which represent real-world problems, and critically interpret their results. The course will cover integer programming and modeling with stochastic programming. The student will become familiar with building mathematical models using Python.

The aim of the course is to prepare students for participation in product development and design of production systems. The product development process and its individual components will be covered, with special emphasis on productivity and design of production systems (flow, outsourcing, material flow and storage). Emphasis is placed on the use of process and system analyzes and calculation models in decision-making.

The course focuses on the principles of logistics and supply management and gives a broad introduction to the field. The course is divided into three topics primarily. It covers purchase operations of services and inventory management. This part is followed by looking into transportation and distribution management. Finally, the environmental impacts of logistics is studies and all the three parts put together into a view of sustainability. The course consists of lectures, exercises, game (the Beer Game) and a management simulation game to give hands on experience on logistics management,

Organization and management systems. The systems approach. Quality management, quality concepts. Historical development of quality management. Quality cost. Quality in manufacturing. x, R, p, c and cusum-chart. Statistical quality control. Tests of hypotheses. Acceptance sampling - OC curves. Inspection planning. Quality systems and quality assurance. Quality handbook and organizing for quality. ISO 9001. Total Quality Management, improvement step by step, motivations theories. Quality tools. Practical assignment: Designing a quality system for a company.

Basic aspects of computational intelligence, which is the study of algorithms that improve automatically through experience.

The purpose of the course is for students to gain an understanding into planning in both production and services. Students are to master both theoretical and practical methods used in planning. The production management section deals with the creation of forecast models for demand, how they drive the preparation of production plans (both products and components), how work plans and work order share resources and how action plans are used to balance out variability. Service plans cover service levels, the use of service planning, shift plans and more.

X

An insight into the structure of innovation, product development and marketing and how to use this methodology as a tool of management in industrial companies. Theory and practical methods of innovation, product development and marketing. Training in project management and how to run integrated projects covering those three areas by solving realistic problems.

Simulation techniques and system modelling find application in fields as diverse as physics, chemistry, biology, economics, medicine, computer science, and engineering. The purpose of this course is to introduce fundamental principles and concepts in the general area of systems modelling and simulation. Topics to be covered in this course are discrete event simulation, statistical modelling, and simulation modelling design, experimental design, model testing and interpretation of simulation results. The maximum likelihood estimation of probability distributions base on real data is presented. The course will also introduce the generation of random variates and testing. Fundamental programming of simulation models in C is covered and specialized simulation packages introduced. The students will complete a real world simulation project where the emphasis will be on manufacturing or service systems.

In this course the focus is on the methodologies of lean management where the goal is to maximize customer value. The methods used are continuous improvements (Kaizen) on the supply chain, standardized work and leveling out work load on people and machines (Heijunka). Students will learn how to use lean methodologies to maximize quality, minimize lead time while simultaneously lowering cost. Continuous flow (Just in time) can shorten the production and process time while problems in the process are identified immediately when they occur (Jidoka). Students will learn value stream mapping for both production and service process, root cause analysis along with other Lean tools such as, 5S, Kaizen Blitz, A3, visual management, Kanban and SMED. In the cours examples of companies that have gone through Lean transformation will be studied.

Business intelligence are the strategies and technologies companies use to collect, interpret and utilize data for decision support. This course goes beyond reports and dashboards and demonstrates how artificial intelligence can help us gain insights and recommend action. The course is comprised of five learning modules: 1.) regression and classification where data is segmented accoring to predetermined labels. 2.)  semi- and un-labelled data where items are grouped based on similarity measures. 3.) Process mining. 4.) Natural language processing, and 5.) Data ethics. Within each learning module students prepare for class and work in teams on a business problem, followed by an individual assessment.

Students choose project topics in consultation with faculty members.

An overview of some of the main concepts, techniques and algorithms in machine learning. Supervised learning and unsupervised learning. Data preprocessing and data visualization. Model evaluation and model selection. Linear regression, nearest neighbors, support vector machines, decision trees and ensemble methods. Deep learning. Cluster analysis and the k-means algorithm. The students implement simple algorithms in Python and learn how to use specialized software packages. At the end of the course the students work on a practical machine learning project.

The course focuses on simple and multiple linear regression as well as analysis of variance (ANOVA), analysis of covariance (ANCOVA) and binomial regression. The course is a natural continuation of a typical introductory course in statistics taught in various departments of the university.

We will discuss methods for estimating parameters in linear models, how to construct confidence intervals and test hypotheses for the parameters, which assumptions need to hold for applying the models and what to do when they are not met.

Students will work on projects using the statistical software R.

Basis in building web projects, focusing on the frontend: HTML, CSS and JavaScript. Standards, practices and what it takes to create a good site. Design, layout and working with design documents. Programming in the interpreted programming language JavaScript, working with the browser and tools related to that. HTTP standard introduced. Projects focus on creating web sites that use what has been taught.

The role of the fish industry in the Icelandic economy. Fish as raw material, its composition, physical and chemical properties. Fish stocks, fishing gear, selectivity. Storage methods on board and after landing. Processing methods, production process and processing equipment for cooling, superchilling, freezing, salting, drying, canning and shell process. Energy and mass balance for each step in the process and the whole process. 

Objective:
To participate in the international Formula Student project by designing and bulding an electric race car with the purpose of participating in international competitions amongst universities. Strict requirement must be followed to participate and this will give the students valuable experience in designing and implementing practical solutions to difficult engineering problems, which is the main objective of the project.

Part A is the project preparation, planning and technical design.

Methods of classical automatic control systems. System models represented by transfer functions and state equations, simulation. System time and frequency responses. Properties of feedback control systems, stability, sensitivity, disturbance rejection, error coefficients. Stability analysis, Routh's stability criterion. Analysis and design using root-locus, lead, lag and PID controllers. Analysis and design in the frequency domain, lead, lag and PID compensators. Computer controlled systems, A/D and D/A converters, transformations of continuous controllers to discrete form. Analysis and design of digital control systems.

The objective of the course is to introduce students to the core concepts of marketing theory and marketing management. The course covers the marketing concept, marketing orientation, and marketing management. Analysis of the marketing environment is also discussed along with analysis of consumer behavior, marketing strategy, competition, and competitive advantage. Finally, the course focuses on the practical application of marketing concepts for success.

Students choose project topics in consultation with faculty members.

Continuation of web programming I, backend programming in node.js, writing and connecting to web services and connections to databases. Frontend libraries/frameworks used to setup a frontend project (React, Ember, Vue). Security issues that need to be considered when writing for the web. Projects focus on creating web sites that use what has been taught.

This course is intended to introduce students to Engineering Psychology and Human Factors. Engineering psychology focuses on how psychological research can be applied to the design and use of tools, technology, and man-made environment. In particular, how the understanding of the capabilities and limitations of human performance through research on perception, cognition, and behavior, can inform design with the aim of usability and accessibility. In this course the emphasis will be on how learning and design intersect. That is, how the design of educational settings and materials influence learning and how design must take prior knowledge and training into account, as well as accommodate learning.

The course is organized as a seminar and students are expected to both contribute and participate in the discussion. Student work mainly consists of readings, discussions in class and independent course work.

This course is intended for students in educational sciences, psychology, and engineering.

Fundamental concepts on approximation and error estimates. Solutions of systems of linear and non-linear equations. PLU decomposition. Interpolating polynomials, spline interpolation and regression. Numerical differentiation and integration. Extrapolation. Numerical solutions of initial value problems of systems of ordinary differential equations. Multistep methods. Numerical solutions to boundary value problems for ordinary differential equations.

Grades are given for programning projects and in total they amount to 30% of the final grade. The student has to receive the minimum grade of 5 for both the projects and the final exam.

Mechanical systems and mechatronics system elements. Mechanism, motors, drives, motion converters, sensors and transducers. Signal processing and microprocessor.

In this course students are introduced to the basic concepts and methods for parametric representation of curves such as the Bezier-, Hermite- and NURBS curves.  Students will learn about the methods for representing three-dimensional wireframe-, solid- and surface models.  The course will cover the use of parameters when developing and creating three-dimensional modeling, the creation of assembly drawings using mating operators and how different engineering software solutions can communicate. 

The course provides a good fundamental overview of the available engineering software solutions – their advantages and limitations – and the students will learn about the current trends in their field, e.g. in the analysis, simulation, prototyping and manufacturing.  The current trends will be indroduced through guest lectures, company visits and a mini-seminar where the students write articles and present new and exciting research or new techniques (based on peer-review papers). 

Concurrently with the lectures, students work on an unstructured engineering project where they will engineer and build a working prototype, write the results in a report and present the results.

To equip students with the ability to apply interdisciplinary knowledge to design fish processing lines and transportation processes for fish products.

Course Content includes:

• Processing stages and equipment for fresh fish processing, freezing, salting, drying, fishmeal production, silage production, etc.
• Energy and mass balances.
• Design criteria for fish processing companies.
• Processing machinery, packaging and storage methods, competitiveness, profitability, quality issues, technological development, and more.
• Calculations of energy and mass balances for each unit operation and complete processing lines.
• Storage conditions (light, humidity, temperature) and key factors affecting changes in fish products during storage, transportation, and distribution.
• Steady-state and time-dependent heat transfer, application of Heisler and Mollier diagrams.

To participate in the international Formula Student project by designing and bulding an electric race car with the purpose of participating in international competitions amongst universities. Strict requirement must be followed to participate and this will give the students valuable experience in designing and implementing practical solutions to difficult engineering problems, which is the main objective of the project.

Part B is the construction of the car and preparing of participation in the international student competition.

Good corporate governance and skilled financial management are the key ingredients for a successfully run corporation.  Finance II builds on the course Finance I, and has its main focus on the corporation and how it is being run from financial management point of view.  The course covers topics in corporate governance, how incentives are embedded in the operation of the firm and what economic and financial outcomes are to be expected from the incentive structure.  The main focus of the course is financial management; the firm’s capital structure, short and long term financing, capital budgeting, dividend policies, short term financial planning as well as financial distress.

The learning path starts with overall orientation and definitions. The focus at first is on challenges in the external environment and how organizational effectiveness can be assessed. Then the focus is on understanding and analysing the structure of organizations and on what should be in the picture when an organizational structure is designed. Influences and challenges related to organizational design and ethical and societal challenges are reviewed. Innovation, knowledge, information, and decision making in the context of organizational change are also covered. Then the impact of artificial intelligence on the structure and working methods of organizations will be discussed.

The focus of the course is on the methodology for developing marketing plans, from market analysis to action plans.  Methods for analyzing market position, for creating marketing strategy, and for the selection of marketing tactics are discussed.
Students develop marketing plans for goods or services, working in groups of four.  Students will contact an organization and develop a marketing plan in alliance with that specific organization.
The course format is a mix of lectures, discussions and project work.

The learning path is in line with the structure of the textbook, starts with overall orientation and definitions. Then the focus is on external and internal analysis in order to assess the strategic situation of companies. After that issues of strategy development are covered and that of strategy implementation. The class will use different teaching methods and the students are required to work on cases and examples of real companies.

Concepts, units, scales and dimensions. Vectors. Kinematics of particles. Particle dynamics, inertia, forces and Newton's laws. Friction. Work and energy, conservation of energy. Momentum, collisions. Systems of particles, center of mass. Rotation of a rigid body. Angular momentum and moment of inertia. Statics. Gravity. Solids and fluids, Bernoulli's equation. Oscillations: Simple, damped and forced. Waves. Sound. Temperature. Ideal gas. Heat and the first law of thermodynamics. Kinetic theory of gases. Entropy and the second law of thermodynamics. Home problems: Once a week the students have to solve homeproblems on the website MasteringPhysics.

Laboratory work: Three exercises, mainly centered on mechanics, where students are trained in handling physical instruments, collecting and inspecting data. Students hand in their lab notebooks for a grade.

Note that the textbook is accessible to students via Canvas free of charge.

The purpose of the course is to prepare students for working in technology-based firms and organizations. The course will give an overview of the management of firms and organizations, the role of engineers and the challenges they face. Students will learn about analysis tools used in decision making, interpret the results, and communicate both orally and in writing.

This is a foundational course in single variable calculus. The prerequisites are high school courses on algebra, trigonometry. derivatives, and integrals. The course aims to create a foundation for understanding of subjects such as natural and physical sciences, engineering, economics, and computer science. Topics of the course include the following:

• Real numbers.
• Limits and continuous functions.
• Differentiable functions, rules for derivatives, derivatives of higher order, applications of differential calculus (extremal value problems, linear approximation).
• Transcendental functions.
• Mean value theorem, theorems of l'Hôpital and Taylor.
• Integration, the definite integral and rules/techniques of integration, primitives, improper integrals.
• Fundamental theorem of calculus.
• Applications of integral calculus: Arc length, area, volume, centroids.
• Ordinary differential equations: First-order separable and homogeneous differential equations, first-order linear equations, second-order linear equations with constant coefficients.
• Sequences and series, convergence tests.
• Power series, Taylor series.

Basics of linear algebra over the reals.  

Subject matter: Systems of linear equations, matrices, Gauss-Jordan reduction.  Vector spaces and their subspaces.  Linearly independent sets, bases and dimension.  Linear maps, range space and nullk space.  The dot product, length and angle measures.  Volumes in higher dimension and the cross product in threedimensional space.  Flats, parametric descriptions and descriptions by equations.  Orthogonal projections and orthonormal bases.  Gram-Schmidt orthogonalization.  Determinants and inverses of matrices.  Eigenvalues, eigenvectors and diagonalization.

The Java programming language will be used to introduce basic practices in computer programming. Practice in programming is scheduled throughout the semester. An emphasis is placed on logical methods for writing program and good documentation. Main ideas related to computers and programming. Classes, objects and methods. Control statements. Strings and arrays, operations and built-in functons. Input and output. Inheritance. Ideas relatied to system design and good practices for program writing. Iteration and recursion. Searching and Sorting.

The goal with this course is to prepare students to approach organisations as sequence of operations.  Organisations will be described as a group of operations that produce value adding work.  Companies will be visited and students will describe their workings as operations sequences.  Methods to describe operations, to analyse operations and to present operation sequences will be introduced and trained.  Students will be introduced to methods of describing, analysing and presenting operations overview.  At course end students should be able to use these methods for both descriptions and analysis and then interpret their results to present both orally and in writing.

The course introduces basic drafting concepts and methods to students.  The aim is to equip the student with the necessary skills needed for creating and reading engineering drawings.  Emphasis is placed developing an understanding of 2D representations of 3D geometries. The student is required to learn the drafting methods and be able to perform them by hand in the final exam.  AutoCAD is used in the course as a drafting tool and students will learn how to use it.  The course is, however, not an AutoCAD course but an engineering drawing course.

Basic concepts in probability and statistics based on univariate calculus. 

Topics: 
Sample space, events, probability, equal probability, independent events, conditional probability, Bayes rule, random variables, distribution, density, joint distribution, independent random variables, condistional distribution, mean, variance, covariance, correlation, law of large numbers, Bernoulli, binomial, Poisson, uniform, exponential and normal random variables. Central limit theorem. Poisson process. Random sample, statistics, the distribution of the sample mean and the sample variance. Point estimate, maximum likelihood estimator, mean square error, bias. Interval estimates and hypotheses testing form normal, binomial and exponential samples. Simple linear regression. Goodness of fit tests, test of independence.

Open and closed sets. Mappings, limits and continuity. Differentiable mappings, partial derivatives and the chain rule. Jacobi matrices. Gradients and directional derivatives. Mixed partial derivatives. Curves. Vector fields and flow. Cylindrical and spherical coordinates. Taylor polynomials. Extreme values and the classification of stationary points. Extreme value problems with constraints. Implicit functions and local inverses. Line integrals, primitive functions and exact differential equations. Double integrals. Improper integrals. Green's theorem. Simply connected domains. Change of variables in double integrals. Multiple integrals. Change of variables in multiple integrals. Surface integrals. Integration of vector fields. The theorems of Stokes and Gauss.

The course will cover various data structures, algorithms and abstract data types. Among the data structures covered are lists, stacks, queues, priority queues, trees, binary trees, binary search trees and heaps along with related algorithms. Various search and sort algorithms will be covered. Algorithms will be analysed for their space and time complexity. There will be small programming assignments in Java using the given data structures and algorithms.

The aim of the course is to prepare students in analysing processes and systems. Description of processes are explained so that process analysis can be performed.  Process discovery and their mapping is explained. Three ways of doing process discovery are explained: fact-based, interview-based and workshop-based.  When processes are mapped they can be analyzed.  The course will go through the qualitative methods of: value-stream-analysis, root-cause-analysis and risk analysis. The quantitative methods of: performance management, flow analysis, queuing theory and simulation will be explained.  The later part of the course will be on systems and system analysis.  System thinking will be explained and how systems can be used to describe the world.  The system analysis methods of causal-loop-diagrams and stocks-and-flows will be explained.  The course will conclude by simulating both processes and systems.

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The aim of this course is offer insights into the analysis and design of technical systems, i.e. systems that use energy, material and information to fulfill given goals. The following topics will be covered in the course:

1) Simple electrical circuits and their use to measure physical properties, such as position, pressure, temperature, and flow.

2) Simple actuators and their use for movement and control of mechanical systems.

3) The basics of automatic control and the use of feedback.

4) The use of microcontrollers for measurement and control of simple technical systems.

5) The process of designing technical systems, including requirements, analysis, implementation, testing, and improvements.

The course is an introductory course in project management. It introduces key concepts of project management and covers context and selection of projects, project planning, project monitoring, management of project teams, and project closure. Students create and execute project plans in groups. Special emphasis is on using of project management for managing technological innovation in organizations.

Functions of a complex variable. Analytic functions. The exponential function, logarithms and roots. Cauchy's Integral Theorem and Cauchy's Integral Formula. Uniform convergence. Power series. Laurent series. Residue integration method. Application of complex function theory to fluid flows. Ordinary differential equations and systems of ordinary differential equations. Linear differential equations with constant coefficients. Systems of linear differential equations. The matrix exponential function. Various methods for obtaining a particular solution. Green's functions for initial value problems. Flows and the phase plane. Nonlinear systems of ordinary differential equations in the plane, equilibrium points, stability and linear approximations. Series solutions and the method of Frobenius. Use of Laplace transforms in solving differential equations.

Introduction to processes and material and energy balance calculations applied to industrial processes. Analysis of gas behavior, gas-liquid systems, and phase equilibrium. Material balances, including reaction systems and multiple-unit systems. Energy balances, including reaction systems and multiple-unit systems, and combined energy-material balances.

In this course, software engineers and computer scientists take the step from programming-in-the-small (i.e. individual developers creating compact modules that solve clearly defined problems) to programming-in-the-large (i.e. teams of developers building complex systems that satisfy vague customer requirements). To deal with the complexities of such projects, this course introduces key software engineering concepts such as agile and plan-driven software process models, requirements engineering, effort estimation, object-oriented analysis and design, software architecture and test-driven development. These concepts are immediately applied in practice as students team up to develop and integrate component-based systems using the Java programming language.

This course will introduce the student to decision and optimization models in operations research. On completing the course the student will be able to formulate, analyze, and solve mathematical models, which represent real-world problems, and critically interpret their results. The course will cover linear programming and the simplex algorithm, as well as related analytical topics. It will also introduce special types of mathematical models, including transportation, assignment, network, and integer programming models. The student will become familiar with a modeling language for linear programming.

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The purpose of the course is to train an engineering approach to experiments and experimental thinking. Experiments are designed, carried out, data collected and processed using statistical methods. Finally, it discussed how conclusions can be drawn from data / information when using experiments in for example product design and the design and operation of production systems.

Course material: Linear and non-linear regression analysis. Analysis of Variances (ANOVA). Design of experiments. Statistical quality control. Non-parametric tests that can be used in data processing. Use of statistical programs when solving tasks.

The objective of the course is that students get the skills to:

1.    Understand the main concepts in accounting, cost theory and investment theory.

2.    Be able to use methods of measuring the economic feasibility of technical projects.

3.    Be able to develop computer models to assess the profitability of investments, the value of companies and pricing of bonds

Among topics included are accounting, cost theory, cash flow analysis, investment theory, measures of profitability including net present value and internal rate of return, and the building of profitability models. The course ends with a group assignment where the students exercise the development of computer models for feasibility assessment of projects.

This course will introduce the student to decision and optimization models in operations research. On completing the course the student will be able to formulate, analyze, and solve mathematical models, which represent real-world problems, and critically interpret their results. The course will cover integer programming and modeling with stochastic programming. The student will become familiar with building mathematical models using Python.

The aim of the course is to prepare students for participation in product development and design of production systems. The product development process and its individual components will be covered, with special emphasis on productivity and design of production systems (flow, outsourcing, material flow and storage). Emphasis is placed on the use of process and system analyzes and calculation models in decision-making.

The course focuses on the principles of logistics and supply management and gives a broad introduction to the field. The course is divided into three topics primarily. It covers purchase operations of services and inventory management. This part is followed by looking into transportation and distribution management. Finally, the environmental impacts of logistics is studies and all the three parts put together into a view of sustainability. The course consists of lectures, exercises, game (the Beer Game) and a management simulation game to give hands on experience on logistics management,

Organization and management systems. The systems approach. Quality management, quality concepts. Historical development of quality management. Quality cost. Quality in manufacturing. x, R, p, c and cusum-chart. Statistical quality control. Tests of hypotheses. Acceptance sampling - OC curves. Inspection planning. Quality systems and quality assurance. Quality handbook and organizing for quality. ISO 9001. Total Quality Management, improvement step by step, motivations theories. Quality tools. Practical assignment: Designing a quality system for a company.

Basic aspects of computational intelligence, which is the study of algorithms that improve automatically through experience.

The purpose of the course is for students to gain an understanding into planning in both production and services. Students are to master both theoretical and practical methods used in planning. The production management section deals with the creation of forecast models for demand, how they drive the preparation of production plans (both products and components), how work plans and work order share resources and how action plans are used to balance out variability. Service plans cover service levels, the use of service planning, shift plans and more.

X

An insight into the structure of innovation, product development and marketing and how to use this methodology as a tool of management in industrial companies. Theory and practical methods of innovation, product development and marketing. Training in project management and how to run integrated projects covering those three areas by solving realistic problems.

Simulation techniques and system modelling find application in fields as diverse as physics, chemistry, biology, economics, medicine, computer science, and engineering. The purpose of this course is to introduce fundamental principles and concepts in the general area of systems modelling and simulation. Topics to be covered in this course are discrete event simulation, statistical modelling, and simulation modelling design, experimental design, model testing and interpretation of simulation results. The maximum likelihood estimation of probability distributions base on real data is presented. The course will also introduce the generation of random variates and testing. Fundamental programming of simulation models in C is covered and specialized simulation packages introduced. The students will complete a real world simulation project where the emphasis will be on manufacturing or service systems.

In this course the focus is on the methodologies of lean management where the goal is to maximize customer value. The methods used are continuous improvements (Kaizen) on the supply chain, standardized work and leveling out work load on people and machines (Heijunka). Students will learn how to use lean methodologies to maximize quality, minimize lead time while simultaneously lowering cost. Continuous flow (Just in time) can shorten the production and process time while problems in the process are identified immediately when they occur (Jidoka). Students will learn value stream mapping for both production and service process, root cause analysis along with other Lean tools such as, 5S, Kaizen Blitz, A3, visual management, Kanban and SMED. In the cours examples of companies that have gone through Lean transformation will be studied.

Business intelligence are the strategies and technologies companies use to collect, interpret and utilize data for decision support. This course goes beyond reports and dashboards and demonstrates how artificial intelligence can help us gain insights and recommend action. The course is comprised of five learning modules: 1.) regression and classification where data is segmented accoring to predetermined labels. 2.)  semi- and un-labelled data where items are grouped based on similarity measures. 3.) Process mining. 4.) Natural language processing, and 5.) Data ethics. Within each learning module students prepare for class and work in teams on a business problem, followed by an individual assessment.

Students choose project topics in consultation with faculty members.

An overview of some of the main concepts, techniques and algorithms in machine learning. Supervised learning and unsupervised learning. Data preprocessing and data visualization. Model evaluation and model selection. Linear regression, nearest neighbors, support vector machines, decision trees and ensemble methods. Deep learning. Cluster analysis and the k-means algorithm. The students implement simple algorithms in Python and learn how to use specialized software packages. At the end of the course the students work on a practical machine learning project.

The course focuses on simple and multiple linear regression as well as analysis of variance (ANOVA), analysis of covariance (ANCOVA) and binomial regression. The course is a natural continuation of a typical introductory course in statistics taught in various departments of the university.

We will discuss methods for estimating parameters in linear models, how to construct confidence intervals and test hypotheses for the parameters, which assumptions need to hold for applying the models and what to do when they are not met.

Students will work on projects using the statistical software R.

Basis in building web projects, focusing on the frontend: HTML, CSS and JavaScript. Standards, practices and what it takes to create a good site. Design, layout and working with design documents. Programming in the interpreted programming language JavaScript, working with the browser and tools related to that. HTTP standard introduced. Projects focus on creating web sites that use what has been taught.

The role of the fish industry in the Icelandic economy. Fish as raw material, its composition, physical and chemical properties. Fish stocks, fishing gear, selectivity. Storage methods on board and after landing. Processing methods, production process and processing equipment for cooling, superchilling, freezing, salting, drying, canning and shell process. Energy and mass balance for each step in the process and the whole process. 

Objective:
To participate in the international Formula Student project by designing and bulding an electric race car with the purpose of participating in international competitions amongst universities. Strict requirement must be followed to participate and this will give the students valuable experience in designing and implementing practical solutions to difficult engineering problems, which is the main objective of the project.

Part A is the project preparation, planning and technical design.

Methods of classical automatic control systems. System models represented by transfer functions and state equations, simulation. System time and frequency responses. Properties of feedback control systems, stability, sensitivity, disturbance rejection, error coefficients. Stability analysis, Routh's stability criterion. Analysis and design using root-locus, lead, lag and PID controllers. Analysis and design in the frequency domain, lead, lag and PID compensators. Computer controlled systems, A/D and D/A converters, transformations of continuous controllers to discrete form. Analysis and design of digital control systems.

The objective of the course is to introduce students to the core concepts of marketing theory and marketing management. The course covers the marketing concept, marketing orientation, and marketing management. Analysis of the marketing environment is also discussed along with analysis of consumer behavior, marketing strategy, competition, and competitive advantage. Finally, the course focuses on the practical application of marketing concepts for success.

Students choose project topics in consultation with faculty members.

Continuation of web programming I, backend programming in node.js, writing and connecting to web services and connections to databases. Frontend libraries/frameworks used to setup a frontend project (React, Ember, Vue). Security issues that need to be considered when writing for the web. Projects focus on creating web sites that use what has been taught.

This course is intended to introduce students to Engineering Psychology and Human Factors. Engineering psychology focuses on how psychological research can be applied to the design and use of tools, technology, and man-made environment. In particular, how the understanding of the capabilities and limitations of human performance through research on perception, cognition, and behavior, can inform design with the aim of usability and accessibility. In this course the emphasis will be on how learning and design intersect. That is, how the design of educational settings and materials influence learning and how design must take prior knowledge and training into account, as well as accommodate learning.

The course is organized as a seminar and students are expected to both contribute and participate in the discussion. Student work mainly consists of readings, discussions in class and independent course work.

This course is intended for students in educational sciences, psychology, and engineering.

Fundamental concepts on approximation and error estimates. Solutions of systems of linear and non-linear equations. PLU decomposition. Interpolating polynomials, spline interpolation and regression. Numerical differentiation and integration. Extrapolation. Numerical solutions of initial value problems of systems of ordinary differential equations. Multistep methods. Numerical solutions to boundary value problems for ordinary differential equations.

Grades are given for programning projects and in total they amount to 30% of the final grade. The student has to receive the minimum grade of 5 for both the projects and the final exam.

Mechanical systems and mechatronics system elements. Mechanism, motors, drives, motion converters, sensors and transducers. Signal processing and microprocessor.

In this course students are introduced to the basic concepts and methods for parametric representation of curves such as the Bezier-, Hermite- and NURBS curves.  Students will learn about the methods for representing three-dimensional wireframe-, solid- and surface models.  The course will cover the use of parameters when developing and creating three-dimensional modeling, the creation of assembly drawings using mating operators and how different engineering software solutions can communicate. 

The course provides a good fundamental overview of the available engineering software solutions – their advantages and limitations – and the students will learn about the current trends in their field, e.g. in the analysis, simulation, prototyping and manufacturing.  The current trends will be indroduced through guest lectures, company visits and a mini-seminar where the students write articles and present new and exciting research or new techniques (based on peer-review papers). 

Concurrently with the lectures, students work on an unstructured engineering project where they will engineer and build a working prototype, write the results in a report and present the results.

To equip students with the ability to apply interdisciplinary knowledge to design fish processing lines and transportation processes for fish products.

Course Content includes:

• Processing stages and equipment for fresh fish processing, freezing, salting, drying, fishmeal production, silage production, etc.
• Energy and mass balances.
• Design criteria for fish processing companies.
• Processing machinery, packaging and storage methods, competitiveness, profitability, quality issues, technological development, and more.
• Calculations of energy and mass balances for each unit operation and complete processing lines.
• Storage conditions (light, humidity, temperature) and key factors affecting changes in fish products during storage, transportation, and distribution.
• Steady-state and time-dependent heat transfer, application of Heisler and Mollier diagrams.

To participate in the international Formula Student project by designing and bulding an electric race car with the purpose of participating in international competitions amongst universities. Strict requirement must be followed to participate and this will give the students valuable experience in designing and implementing practical solutions to difficult engineering problems, which is the main objective of the project.

Part B is the construction of the car and preparing of participation in the international student competition.

Good corporate governance and skilled financial management are the key ingredients for a successfully run corporation.  Finance II builds on the course Finance I, and has its main focus on the corporation and how it is being run from financial management point of view.  The course covers topics in corporate governance, how incentives are embedded in the operation of the firm and what economic and financial outcomes are to be expected from the incentive structure.  The main focus of the course is financial management; the firm’s capital structure, short and long term financing, capital budgeting, dividend policies, short term financial planning as well as financial distress.

The learning path starts with overall orientation and definitions. The focus at first is on challenges in the external environment and how organizational effectiveness can be assessed. Then the focus is on understanding and analysing the structure of organizations and on what should be in the picture when an organizational structure is designed. Influences and challenges related to organizational design and ethical and societal challenges are reviewed. Innovation, knowledge, information, and decision making in the context of organizational change are also covered. Then the impact of artificial intelligence on the structure and working methods of organizations will be discussed.

The focus of the course is on the methodology for developing marketing plans, from market analysis to action plans.  Methods for analyzing market position, for creating marketing strategy, and for the selection of marketing tactics are discussed.
Students develop marketing plans for goods or services, working in groups of four.  Students will contact an organization and develop a marketing plan in alliance with that specific organization.
The course format is a mix of lectures, discussions and project work.

The learning path is in line with the structure of the textbook, starts with overall orientation and definitions. Then the focus is on external and internal analysis in order to assess the strategic situation of companies. After that issues of strategy development are covered and that of strategy implementation. The class will use different teaching methods and the students are required to work on cases and examples of real companies.