Final project (REI441L)

• The topic of the Master's thesis must be chosen under the guidance of the supervisor and the Faculty Coordinator of the student. The thesis represents 30 or 60 credits. All Master's student have been assigned to a Faculty Coordinator from the beginning of their studies, who advises the student regarding the organization of the program. If a student does not have a supervisor for the final project, he / she must turn to the Faculty Coordinator for assistance.
• The choice of topic is primarily the responsibility of the student in collaboration with his or her project supervisor. The topic of the project should fall within the student's area of study, i.e. course of study and chosen specialisation.
• The master’s student writes a thesis according to the School’s template and defends it in a master’s defense.
• Final project exam is divided into two parts: Oral examination and open lecture
• Present at the oral exam is the student, supervisor, examiner and members of the Master's committee. The student presents a brief introduction on his / her project. It is important that the objectives and research question(s) are clearly stated, and that main findings and lessons to be drawn from the project are discussed.
• The student delivers a thesis and a project poster.
• According to the rules of the Master's program, all students who intend to graduate from the School of Engineering and Natural Sciences need to give a public lecture on their final project.
• All students graduating from the University of Iceland shall submit an electronic copy of their final Master's thesis to Skemman.is. Skemman is the digital repository for all Icelandic universities and is maintained by the National and University Library.
• According to regulations of University of Iceland all MS thesis should have open access after they have been submitted to Skemman.

Learning Outcomes:

Upon completion of an MS thesis, the student should be able to:

• Formulate engineering design project  / research questions
• Use an appropriate theoretical framework to shed light on his / her topic
• Analyze and solve engineering tasks in a specialized field.
• Perform a literature search and a thorough review of the literature.
• Demonstrate initiative and independent creative thinking.
• Use economic methodology to answer a specific research question
• Competently discuss the current knowledge within the field and contribute to it with own research
• Work with results, analyze uncertainties and limitations and interpret results.
• Assess the scope of a research project and plan the work accordingly
• Effectively display results and provide logical reasoning and relate results to the state of knowledge.

Selected Topics in Mechanical Engineering (VÉL049F)

Lectures on and study of selected topics in current research and recent development in the field of Mechanical engineering. Topics may vary.

Students contact the teacher and the chair of department regarding registration for the course.

Usable Privacy and Security (HBV507M)

Survey of the field of usable security and privacy with an emphasis on emerging technologies. Topics include authentication, location privacy, social network privacy, behavioral advertising, - health privacy, anonymity, cryptocurrency, technical writing and ethical conduct of usable privacy and security research.

Computational Intelligence (IÐN102M)

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

Time Series Analysis (IÐN113F)

ARMAX and other similar time series models. Non-stationary time series. Correlation and spectral analysis. Parameter estimation, parametric and non-parametric approaches, Least Squares and Maximum Likelihood. Model validation methods. Models with time dependent parameters. Numerical methods for minimization. Outlier detection and interpolation. Introduction to nonlinear time series models. Discrete state space models. Discrete state space models. Extensive use of MATLAB, especially the System Identification Toolbox.

Performance analysis of computer systems (REI503M)

Usually taught every second year.

This course provides students with an introduction to modeling and performance evaluation of computer and communication systems. Large-scale distributed computer systems process arriving requests, e.g., web page queries, in parallel in order to optimize performance metrics, such as response time and user satisfaction. Other important performance metrics include throughput and service-level agreement in general. This course covers basic mathematical tools needed to evaluate such dynamic systems and to understand the strengths and weaknesses, for example in different designs, scheduling disciplines, and operating policies. The approach is based on operations research methods, in particular, queueing theory and Markov processes (previous knowledge of these methods is not required).

Attendance is strongly recommended.

Cloud Computing and Big Data (REI504M)

Overview of high performance computing (HPC) and “Big Data”, HPC environments with computing, network and storage resources, overview of parallel programming. Storage infrastructures and services for Big Data, Big Data analytics, the map-reduce paradigm, structured and unstructured data. Practical exercises: (A) Students will use the Amazon Web Services (AWS) cloud or equivalent to set up a multi-computer web service and an associated multi-computer testing application. (B) Students will get hands on experience of processing large data sets using map-reduce techniques with AWS.

Machine Learning (REI505M)

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.

Machine Learning for Earth Observation powered by Supercomputers (REI506M)

This course exposes the students to the physical principles underlying satellite observations of Earth by passive sensors, as well as parallel Deep Learning (DL) algorithms that scale on High Performance Computing (HPC) systems. 

For the different theoretical concepts (represented by 4 modules), the course provides hands-on exercises. These exercises are part of a project in the context of Remote Sensing (RS) image classification that the students are asked to develop during the whole duration of the course.

Theory of linear models (STÆ310M)

Simple and multiple linear regression, analysis of variance and covariance, inference, variances and covariances of estimators, influence and diagnostic analyses using residual and influence measures, simultaneous inference. General linear models as projections with ANOVA as special case, simultaneous inference of estimable functions. R is used in assignments. Solutions to assignments are returned in LaTeX and PDF format.

In addition selected topics will be visited, e.g. generalized linear models (GLMs), nonlinear regression and/or random/mixed effects models and/or bootstrap methods etc.

Students will present solutions to individually assigned
projects/exercises, each of which is handed in earlier through a web-page.

This course is taught in semesters of even-numbered years.

Applied Linear Statistical Models (STÆ312M)

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.

Theoretical Statistics (STÆ313M)

Likelihood, Sufficient Statistic, Sufficiency Principle, Nuisance Parameter, Conditioning Principle, Invariance Principle, Likelihood Theory. Hypothesis Testing, Simple and Composite Hypothesis, The Neyman-Pearson Lemma, Power, UMP-Test, Invariant Tests. Permutation Tests, Rank Tests. Interval Estimation, Confidence Interval, Confidence, Confidence Region. Point Estimation, Bias, Mean Square Error. Assignments are returned using LaTeX and consitute 20% of the final grade.

Stochastic Processes (STÆ415M)

Introduction to stochastic processes with main emphasis on Markov chains.

Subject matter: Hitting time, classification of states, irreducibility, period, recurrence (positive and null), transience, regeneration, coupling, stationarity, time-reversibility, coupling from the past, branching processes, queues, martingales, Brownian motion.

Numerical Linear Algebra (STÆ511M)

Iterative methods for linear systems of equations.  Decompositions of matrices: QR, Cholesky, Jordan, Schur, spectral and singular value decomposition (SVD) and their applications.  Discrete Fourier transform (DFT) and the fast Fourier transform (FFT).  Discrete cosine transform (DCT) in two-dimensions and its application for the compression of images (JPEG) and audio (MP3, AAC).  Sparse matrices and their representation.

Special emphasis will be on the application and implementation of the methods studied.

Bayesian Data Analysis (STÆ529M)

Goal: To train students in applying methods of Bayesian statistics for analysis of data. Topics: Theory of Bayesian inference, prior distributions, data distributions and posterior distributions. Bayesian inference  for parameters of univariate and multivariate distributions: binomial; normal; Poisson; exponential; multivariate normal; multinomial. Model checking and model comparison: Bayesian p-values; deviance information criterion (DIC). Bayesian computation: Markov chain Monte Carlo (MCMC) methods; the Gibbs sampler; the Metropolis-Hastings algorithm; convergence diagnostistics. Linear models: normal linear models; hierarchical linear models; generalized linear models. Emphasis on data analysis using software, e.g. Matlab and R.

Algorithms in Bioinformatics (TÖL504M)

This course will cover the algorithmic aspects of bioinformatic. We  will start with an introduction to genomics and algorithms for students new to the field. The course is divided into modules, each module covers a single problem in bioinformatics that will be motivated based on current research. The module will then cover algorithms that solve the problem and variations on the problem. The problems covered are motif finding, edit distance in strings, sequence alignment, clustering, sequencing and assembly and finally computational methods for high throughput sequencing (HTS).

Computational Structural Mechanics (VÉL103M)

The aim of this course is to give students an exposure to the theoretical basis of the finite element method and its implementation principles. Furthermore, to introduce the use of available finite element application software for solving real-life engineering problems.

The course covers such topics as: stiffness matrices, elements stiffness matrix, system stiffness matrix, local and global stiffness, shape functions, isoparametric formulation and numerical integration. Various elements are studied, such as, trusses and beams, plane elements, 3D elements, plates and shells. Students mostly solve problems in solid mechanics (stress analysis) but can choose to work on a design project in other areas, such as vibrations or heat transfer.

The course includes class lectures and work sessions where students solve problems, both in python (can also choose matlab) and in the commercial software Ansys, under the supervison of the instructor. There is extensive use of Python (Matlab) and Ansys in solving homework problems and semester projects.

Design Optimization (VÉL113F)

Optimum design concepts. Fundamentals of linear and nonlinear programming, constrained and unconstrained optimum design problems. Simulated annealing and genetic algorithms. Project and applications to realistic engineering design problems.

Thesis skills: project management, writing skills and presentation (VON001F)

Introduction to the scientific method. Ethics of science and within the university community.
The role of the student, advisors and external examiner. Effective and honest communications.
Conducting a literature review, using bibliographic databases and reference handling. Thesis structure, formulating research questions, writing and argumentation. How scientific writing differs from general purpose writing. Writing a MS study plan and proposal. Practical skills for presenting tables and figures, layout, fonts and colors. Presentation skills. Project management for a thesis, how to divide a large project into smaller tasks, setting a work plan and following a timeline. Life after graduate school and being employable.

Final project (REI441L)

• The topic of the Master's thesis must be chosen under the guidance of the supervisor and the Faculty Coordinator of the student. The thesis represents 30 or 60 credits. All Master's student have been assigned to a Faculty Coordinator from the beginning of their studies, who advises the student regarding the organization of the program. If a student does not have a supervisor for the final project, he / she must turn to the Faculty Coordinator for assistance.
• The choice of topic is primarily the responsibility of the student in collaboration with his or her project supervisor. The topic of the project should fall within the student's area of study, i.e. course of study and chosen specialisation.
• The master’s student writes a thesis according to the School’s template and defends it in a master’s defense.
• Final project exam is divided into two parts: Oral examination and open lecture
• Present at the oral exam is the student, supervisor, examiner and members of the Master's committee. The student presents a brief introduction on his / her project. It is important that the objectives and research question(s) are clearly stated, and that main findings and lessons to be drawn from the project are discussed.
• The student delivers a thesis and a project poster.
• According to the rules of the Master's program, all students who intend to graduate from the School of Engineering and Natural Sciences need to give a public lecture on their final project.
• All students graduating from the University of Iceland shall submit an electronic copy of their final Master's thesis to Skemman.is. Skemman is the digital repository for all Icelandic universities and is maintained by the National and University Library.
• According to regulations of University of Iceland all MS thesis should have open access after they have been submitted to Skemman.

Learning Outcomes:

Upon completion of an MS thesis, the student should be able to:

• Formulate engineering design project  / research questions
• Use an appropriate theoretical framework to shed light on his / her topic
• Analyze and solve engineering tasks in a specialized field.
• Perform a literature search and a thorough review of the literature.
• Demonstrate initiative and independent creative thinking.
• Use economic methodology to answer a specific research question
• Competently discuss the current knowledge within the field and contribute to it with own research
• Work with results, analyze uncertainties and limitations and interpret results.
• Assess the scope of a research project and plan the work accordingly
• Effectively display results and provide logical reasoning and relate results to the state of knowledge.

Algorithms in the real world (TÖL608M)

The course will cover the design and analysis of algorithms, with emphasis on algorithms for large datasets and real world applications.

The algorithms covered will be drawn from various subfields, e.g. data and text compression, error correcting codes, large scale text search and search engines, parallel programming, GPU programming, streaming algorithms, probabilistic algorithms, nearest neighbor search in high dimensional datasets.

Software Testing (HBV205M)

Usually taught every second year (typically in spring of odd years, but this is subject to change in 2024).

This course covers testing of software. Besides basic foundations, this includes both dynamic testing where the software under test is executed and static approaches where software and other artefacts produced during software development are investigated without executing them. The focus of this course is, however, on dynamic testing. The different levels of testing (component test, integration test, system and acceptance test) and types of testing (functional, non-functional, structural and change-related) are covered as well as different test design techniques (black box test and white box test). Furthermore, test management and principles of test tools are discussed. In addition, selected advanced topics may be covered (for example, test languages, testing of object-oriented software, test process improvement, agile testing). The covered topics are a superset of the International Software Testing Qualifications Board's (ISTQB) certified tester foundation level syllabus.

The first part of the course is based on flipped-classroom style weekly reading, videos and assignments. In the second part, students work independently on some project related to software testing.

Note: while this is an "M" course, it is rather on MSc. level. BSc. students who take this course need to be very advanced in their BSc. studies, i.e. have experience in programming languages, software development and applying it in some software project, but should also be familiar with theoretical concepts from automata theory.

Also, BSc. students should not take this course, if they know that they are going to continue with MSc. studies, because they might then experience a lack of suitable courses in their MSc. studies.

Ethnographic Approaches to Cybersecurity (HBV604M)

As the world's technological complexity increases, cybersecurity is more important than ever. However, not all security vulnerabilities are technological. In this course, we will explore how things like human behavior, culture, and processes can impact the security of a technical environment in significant ways. The first half of the semester will lay the groundwork for these concepts, and as we are building our skills we will hear from speakers doing ethnographic and security work in the global technology industry today. In the second half, we will identify and explore non-technical security vulnerabilities in the world around us, and build compelling stories around the consequences of those vulnerabilities.

This course will be of special interest to Computer Science students with an interest in cybersecurity, HCI, system architecture, or design thinking. In addition, it may also be of interest to business students focused on technology, international policy students focused on security, social anthropology or folklore/folkloristics students, and those interested in regional (e.g. Nordic, Arctic, etc.) studies.

Selected Topics in Time Series Analysis and Control Theory (IÐN213F)

Goal: To introduce main methods used in estimating parameters in time dependent systems and to control systems under stochastic load. Content: The course is partly based on students giving their own lectures on selected topics chosen in cooperation with the instructors. Examples of topics are e.g. different recursive parameter estimation methods, choice of input signals, non-linear time series models, use of stochastic differential equations in modelling, stochastic control including Linear Quadratic Gaussian, Minimum variance, Generalized Predictive Control, Adaptive Control and Fuzzy Control. Extensive use of Matlab.Evaluation of the cours is based on the lectures and home assignments.

Introduction to Systems Biology (LVF601M)

Systems biology is an interdisciplinary field that studies the biological phenomena that emerge from multiple interacting biological elements. Understanding how biological systems change across time is a particular focus of systems biology. In this course, we will prioritize aspects of systems biology relevant to human health and disease.

This course provides an introduction to 1) basic principles in modelling molecular networks, both gene regulatory and metabolic networks; 2) cellular phenomena that support homeostasis like tissue morphogenesis and microbiome resilience, and 3) analysis of molecular patterns found in genomics data at population scale relevant to human disease such as patient classification and biomarker discovery. In this manner, the course covers the three major scales in systems biology: molecules, cells and organisms.

The course activities include reading and interpreting scientific papers, implementation of computational algorithms, working on a research project and presentation of scientific results.

Lectures will comprise of both (1) presentations on foundational concepts and (2) hands-on sessions using Python as the programming language. The course will be taught in English.

High Performance Computing (REI204M)

Design of parallel computers and parallel programming models. Shared memory architecture. Message passing and distributed memory architecture. Parallel programming of computer clusters using MPI and multicore programming using OpenMP. Parallel algorithms for sorting, searching, linear algebra, and various graph problems.

Course topics will be very similar like HPC in Fall 2019:

http://www.morrisriedel.de/hpc-course-fall-2019

Positioning in the Field of High-Performance Computing (HPC)

• Consists of techniques for programming & using large-scale HPC Systems
• Approach: Get a broad understanding of what HPC is and what can be done
• Goal: Train general HPC techniques and systems and selected details of domain-specific applications

Course Motivation

Parallel processing and distributed computing

• Matured over the past three decades
• Both emerged as a well-developed field in computer science
• Still a lot of innovation, e.g. from hardware/software

‘Scientific computing‘ with Maple, Matlab, etc.

• Performed on small (‘serial‘) computing machines like Desktop PCs or Laptops
• An increasing number of cores enables ‘better scientific computing‘ today
• Good for small & fewer complex applications, quickly reach memory limits

‘Advanced scientific computing‘

• Used with computational simulations and large-scale machine & deep learning
• Performed on large parallel computers; often scientific domain-specific approaches
• Use orders of magnitude multi-core chips & large memory & specific many-core chips
• Enables ‘simulations of reality‘ - often based on known physical laws and numerical methods

Random Effects Models (STÆ004F)

The focus of this course is on Bayesian latent Gaussian models (BLGMs) which are a class of Bayesian hierarchical models and applications of these models. The main topics are three types of BLGMs: (i) Bayesian Gaussian—Gaussian models, (ii) BLGMs with a univariate link function, and (iii) BLGMs with a multivariate link function, as well as prior densities for BLGMs and posterior computation for BLGMs. In the first part of the course, the basics of these models is covered and homework assignments will be given on these topics. In the second part of the course, the focus is on a project, in which data are analyzed using BLGMs. Each student can contribute data that she or he wishes to analyze. The material in the course is based on a theoretical background. However, the focus on data analysis is strong, and computation and programming play a large role in the course. Thus, the course will be useful to students in their future projects involving data analysis.

Linear regression models, the multiple normal distribution, hierarchical models, fixed and random effect models, restricted maximum likelihood estimation, best linear unbiased estimators, Bayesian inference, statistical decision theory, Markov chains,  Monte Carlo integration, importance sampling, Markov chain Monte Carlo, Gibbs sampling, the Metropolis-Hastings algorithm.

Introduction to Measure-Theoretic Probability (STÆ418M)

Probability based on measure-theory.

Subject matter: Probability, extension theorems, independence, expectation. The Borel-Cantelli theorem and the Kolmogorov 0-1 law. Inequalities and the weak and strong laws of large numbers. Convergence pointwise, in probability, with probability one, in distribtution, and in total variation. Coupling methods. The central limit theorem. Conditional probability and expectation.

Computational Fluid Dynamics (VÉL215F)

The main purpose is to develop methods of predicting numerical solutions in fluid mechanics and heat transfer. Especially of predicting boundary layer phenomena and modelling of turbulence transport properties. Both finite volume and finite difference methods are demonstrated. Solution of non-linear equations and stability criterium. Emphasis is laid on solution of practical problems.

The course is taught every other year on odd numbered years.

Algorithms in Bioinformatics (TÖL604M)

This course will cover the algorithmic aspects of bioinformatic. We  will start with an introduction to genomics and algorithms for students new to the field. The course is divided into modules, each module covers a single problem in bioinformatics that will be motivated based on current research. The module will then cover algorithms that solve the problem and variations on the problem. The problems covered are motif finding, edit distance in strings, sequence alignment, clustering, sequencing and assembly and finally computational methods for high throughput sequencing (HTS).