Mentor in Sprettur (GKY001M)

In the course, the student's task consists in being a mentor for participants that are upper secondary school students and university students in the project "Sprettur". Mentors' main role is to support and encourage participants in their studies and social life. As well as creating a constructive relationship with the participants, being a positive role model, and participating in events organized in Sprettur. The mentor role centers around building relationships and spending meaningful time together with the commitment to support participants. 

Sprettur is a project that supports foreign students that would like to receive extra support to improve their performance and sustainability in their studies. The students in this course are mentors of the participants and are paired together based on a common field of interest. Mentors work in groups to support participants. Mentors plan activities with participants and spend three hours a month (from August to May) with Sprettur’s participants, three hours a month in a study group and attend five seminars that are spread over the school year. Students submit journal entries on Canvas once per semester and have to design and implement a learning experience for participants in Sprettur once in the course. Diary entries are based on reading material and students' reflections on the mentorship. Compulsory attendance in events, study groups, and seminars. The course is taught in Icelandic and English. 

Students fill out a digital application form and the teacher will contact them. 

More information about Sprettur can be found here: www.hi.is/sprettur  

Quantum Computing and Quantum Cryptography (TÖL031M)

Students will explore the fundamentals of quantum computing and its applications in computing and cryptography through hands-on programming exercises. Beginning with classical computing concepts, they will progress to quantum gates, quantum algorithms, and quantum cryptographic schemes such as quantum key distribution (QKD). Using tools like Qiskit, students will implement and simulate quantum circuits. Finally, students will complete a project that applies what they have learned to address a problem in quantum computing or quantum cryptography.

Topics covered:

• Introduction to classical and quantum computing
• Quantum mechanics fundamentals for computing 
• Quantum gates and circuits
• Quantum algorithms
• Introduction to quantum cryptography
• Error-correcting codes
• Quantum cryptography revisited
• Applications and impact of quantum computing and quantum cryptography

Fundamentals of Ethical Hacking (TÖL605M)

Ethical hacking is the discipline of attacker perspective security analysis. Using tools and techniques used by malicious hackers, ethical hackers gain unique and unique and valuable insights used for improving system security.

This course will introduce students to the fundamentals of ethical hacking tools and techniques. Students will begin by configuring a secure lab using virtualisation tools. Then every two weeks a new stage of the ethical hacking methodology and some related tools will be introduced. The students will submit weekly lab reports evidencing their theoretical and practical understanding of the methodology and tools. 

For the best experience, students should own a PC able to run virtualisation software with an x86 image. Mac users with Apple CPU or those with PCs whose CPUs lack virtualisation support or who have limited RAM might have issues running a stable lab on their machine. Students who lack suitable hardware can use VMs on the department's cyber security server infrastructure.

Software Maintenance (HBV103M)

The first part of the course is based on flipped-classroom style weekly reading, videos and assignments on:

• Evolution of Software and Lehman’s laws,
• Maintenance processes,
• Metrics useful for maintenance,
• Software analysis,
• Re-engineering,
• Reverse engineering,
• Code Smells & Refactoring,
• Basics of (Regression-)Testing,
• Design principles to support change & Design Patterns,
• Tools for software maintenance (including advanced features of an IDE).

In the second part of this course, these techniques will be applied in order to maintain a real legacy software written in Java.

Note: while this is an "M" course, it is rather on MSc. level. BSc. students who take this course need to be advanced in their BSc. studies and you need solid Java programming experience: we will maintain a complex software and being able to understand how such a grown software works and to fix bugs is even more difficult to write such a software from scratch. Hence, you should have passed HBV501G Software Project 1, preferably even HBV601G Software Project 2. (It is impossible to maintain a software if you would not even be able to develop it.) 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.

Software Quality Management (HBV505M)

The course is based on flipped-classroom style weekly reading, videos and assignments on software quality management-related aspects of the Software Development Life Cycle (SDLC) and by covering some parts of DevOps also Application Lifecycle Management (ALM). In parallel to theoretical concepts, the application of source code-centric tools relevant for quality management is trained by applying them to a codebase throughout the course using the ALM tool GitLab. The concepts and tools are independent from a particular software development process and cover:

• Software Quality Foundations, Software Quality Models.
• Configuration management (CM) and traceability:
  • Version management (e.g. Git),
  • Change management (e.g. issue tracker),
  • Build management (e.g. Maven),
  • Release management
• Continuous integration (CI) (e.g. using GitLab pipelines).
• Integrating testing into a CI pipeline (e.g. using JUnit and GitLab).
• Reviews (e.g. tool-based code review)
• Static analysis (e.g. SonarCloud)
• Metrics for quality management (product and process metrics).
• Quality standards:
  • Software Life Cycle Processes,
  • Software Process Improvement and maturity assessment (e.g. CMMI).
• Using a Wiki to create a quality plan and other documentation.

Students chose their own codebase (e.g. from the Software Project 1 or 2 course) to which they apply the concepts and tools tought in this course. While the teaching material and tools assumes Java as programming language, students are welcome to use a codebase in another programming language.

Software quality in agile development processes is covered by student presentations at the end of the course.

Note: while this is an "M" course, it is rather on MSc. level. BSc. students who take this course need to be advanced in their BSc. studies and you need Java programming experience. Hence, you should have passed HBV501G Software Project 1, preferably even HBV601G Software Project 2.
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.

Quality Management (IÐN101M)

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.

Computational Intelligence (IÐN102M)

In designing intelligent systems, there is a need for autonomous systems that can learn to make good decisions from their own experience. This course introduces students to reinforcement learning—a computational approach in which systems automatically improve their performance through interaction and reward, without explicit instruction. The course covers Markov decision processes, dynamic programming, Monte Carlo methods, policy optimization, planning, search methods, and deep neural networks as function approximators. The curriculum combines theoretical discussion and project work, giving students the opportunity to model, analyze, and solve mathematical models for decision-making problems. Emphasis is also placed on the iterative process and on critical reflection regarding what it means for algorithms to learn and the impact that autonomous computational intelligence might have on science and society.

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.

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 (TÖV605M)

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.

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.

Introduction to Information Security (TÖL029M)

This course provides the foundation for understanding the key issues associated with protecting information assets, determining the levels of protection and response to security incidents, and designing a consistent, reasonable information security system, with appropriate intrusion detection and reporting features.  The purpose of the course is to provide the student with an overview of the field of information security and assurance. Students will be exposed to the spectrum of security activities, methods, methodologies, and procedures.  Coverage will include inspection and protection of information assets, detection of and reaction to threats to information assets, and examination of pre- and post-incident procedures, technical and managerial responses and an overview of the information security planning and staffing functions.  Topics include risk assessment, authentication, securing web, application security, privacy and data protection, introduction to cryptography, security architecture, firewalls and other devices and network topologies.

From an Idea to Reality (TÖL109F)

The course has been in development for over a decade under the name "Computer Systems and Marketing". 

The course is based on teacher lectures and individual and group projects that help students to think outside the box. Teacher will tell of his experiences of making ideas become real and have a special focus on the mistakes that have been made along with the successful stories.
The final project of the course is a business plan that will be presented in an oral final exam. The business plan focuses on the idea of each student and how the student plans to realize his project.

Human Computer Interaction (TÖL502M)

Usually taught every second year.

The objective of the course is to allow students to examine more closely than is done in the introductory course Graphical User Interface Programming  HBV201G specific factors of HCI. The factors covered are user interface design using prototyping, programming of smart devices and users‘ acceptability of the software. There will be emphasis on different techniques and tools to develop prototypes. Also, on the design of user interfaces and native programming for smart phones or pads. The development process will be aimed at ensuring accessibility and acceptability of users. Students work on small projects individually, or on larger projects in groups. 

Distributed Systems (TÖL503M)

Usually taught every second year.

This course covers concepts of distributed systems and their application. Besides foundations on characteristics and models of distributed systems, networking and security, this includes network-based low-level interprocess communication, high-level remote procedure calls, the distributed object model and remote method invocation, services relevant in distributed systems (such as name services or distributed file systems), selected topics of distributed algorithms and their implementation (such as coordination, agreement,  time, replication). Furthermore, special types of distributed systems may be covered (such as peer-to-peer systems, Cloud and Grid computing).  Current technologies (such as Java RMI, Web Services, gRPC) are used as case study and as platform for developing distributed applications using high-level programming languages (such as Java).

Note: while this is an "M" course, it is rather on MSc. level. BSc. students who take this course need to be advanced in their BSc. studies. (E.g. we will implement middleware in Java, so you should have programming experience well beyond "TÖL101G Computer Science 1". As a middleware adds functionality on top of an Operating System, you should have also passed TÖL401G Operating Systems.)

Introduction to deep neural networks (TÖL506M)

In this course we cover deep neural networks and methods related to them. We study networks and methods for image, sound and text analysis. The focus will be on applications and students will present either a project or a recent paper in this field.

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).

Computer Graphics (TÖL105M)

The main emphasis is on fundamental concepts and mathematics for 3D computer graphics. Two and three-dimensional transformations. Viewing projections. Light and the shading of objects. Texture mapping, blending, bump maps. Programmable shaders. Curves and surfaces. Programming assignments in WebGL.

Theoretical Foundations of Innovation and Entrepreneurship (VIÐ186F)

The aim of the course is to give students an overview of the theoretical foundations of innovation and entrepreneurship and prepare them for further studies, both academic and applied.

The course will cover the most prominent theories and unresolved questions within the field; students will review the latest academic articles and learn about tools to analyze major innovation trends.

Final project (TÖL431L)

• 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.

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.

Programming Projects on Internet of Things (TÖL103M)

This course offers a hands-on introduction to programming small devices (microcontrollers, single-board computers) used in the context of Internet of Things, sensor networking, and home automation.

Such devices are often equipped with a large number of I/O pins, some RAM and flash memory, and wireless communication capabilities (e.g. WiFi and/or Bluetooth), making them attractive for tasks involving data acquisition,
processing, and wireless communication.

The course consists of bi-weekly programming tasks dealing with serial communication, data acquisition and analysis, programming of real-time O/S (RTOS), wireless communication and TCP/IP client-servers paradigm.

The biweekly assignments lead to a final project.

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.

Governance of the Internet (TÖL212F)

This course aims to provide a comprehensive overview of Internet governance and challenges in regulating cyberspace. Topics include the evolution of formalized internet governance, and the role of national governments, supranational organizations and private corporations in shaping the international regulatory framework for Internet governance.

The course will also explore the balance of privacy versus security, including the role of regulations such as GDPR and NIS2 to establish minimum requirements of “privacy” or “security” by design. The emergence of a “cyber-public” space has created new challenges for enforcing laws ranging from copyright to illegal content. Furthermore, the question of public/private cooperation in fighting against cybercrime and regulatory challenges regarding the emergence of cyberwarfare will be examined.

This course will be taught as a series of weekly lectures and bi-weekly seminars for student discussions and debates in internet governance topics.

Fundaments of the Internet (RAF617M)

Modern day telecommunications are characterised by the fact that communicated data is carried on fixed telecommunications infrastructure on a majority of the path between tranmitter and receiver. Wireless infrastructure often conveys the signals at the end of the path. This yields both high speed and the comfort of wireless communications. It is of utmost importance for engineers working in telecoms to have a fundamental knowledge of fixed networks and the range of technologies deployed.

In this course, the structure of fixed backbone and access networks will be described. Optical fibre and related technologies will be introduced, e.g. DWDM, SDH, Ethernet, ATM and MPLS-TP. Access network technologies on copper, coax and optical fibres will be treated, e.g. ADSL. VDSL, G.fast and  DOCSIS. Different FTTH (Fibre to the Home) technologies will be treated such as PON (Passive Optical Network), Active Ethernet and point-to-point Ethernet. 

IP (Internet Protocol) has become a fundamental technology for modern fixed networks. IP native technologies will be described from the physical to the application layer. On the link layer, Ethernet will be in the focus as well as MPLS. Circuit and packet switching will be treated as well as circuit and packet orientation of networks. Sevices such as PSTN, VoIP, OTT and P2P will be treated. Backhauling of wireless networks such as mobile networks and Wi-Fi will also be treated.
In introduction to network virtualisation and network function virtualisation (NFV) will be given as well as software defined networking (SDN). Legal and regulatory aspects will be introduced and aspects like network neutrality discfussed. Important players and stakeholders will be discussed, e.g. Google, Apple, Microsoft, Netflix and telecommunications service providers.
Finally, local area networks will be discussed, home networking, smart homes, set-top-boxes, NAS, PLC, plastic optical fibres, MOCA and Wi-Fi introduced.

The teaching form will be lectures and projects on IP communications will be worked. Students will write four papers on selected subjects and give presentations. 

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).

Software Quality Management (HBV204M)

The course is based on flipped-classroom style weekly reading, videos and assignments on software quality management-related aspects of the Software Development Life Cycle (SDLC) and by covering some parts of DevOps also Application Lifecycle Management (ALM). In parallel to theoretical concepts, the application of source code-centric tools relevant for quality management is trained by applying them to a codebase throughout the course using the ALM tool GitLab. The concepts and tools are independent from a particular software development process and cover:

• Software Quality Foundations, Software Quality Models.
• Configuration management (CM) and traceability:
  • Version management (e.g. Git),
  • Change management (e.g. issue tracker),
  • Build management (e.g. Maven),
  • Release management
• Continuous integration (CI) (e.g. using GitLab pipelines).
• Integrating testing into a CI pipeline (e.g. using JUnit and GitLab).
• Reviews (e.g. tool-based code review)
• Static analysis (e.g. SonarCloud)
• Metrics for quality management (product and process metrics).
• Quality standards:
  • Software Life Cycle Processes,
  • Software Process Improvement and maturity assessment (e.g. CMMI).
• Using a Wiki to create a quality plan and other documentation.

Students chose their own codebase (e.g. from the Software Project 1 or 2 course) to which they apply the concepts and tools tought in this course. While the teaching material and tools assumes Java as programming language, students are welcome to use a codebase in another programming language.

Software quality in agile development processes is covered by student presentations at the end of the course.

Note: while this is an "M" course, it is rather on MSc. level. BSc. students who take this course need to be advanced in their BSc. studies and you need Java programming experience. Hence, you should have passed HBV501G Software Project 1, preferably even HBV601G Software Project 2.
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.

Software Testing (HBV205M)

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.

Field Course in Innovation and Entrepreneurship (II) (IÐN216F)

The course is a continuation of the course "Field Course in Innovation and Entrepreneurship (I)". This part of the course consists of detailed development of the business model related to a particular business opportunity. This work takes place in groups, where cross-disciplinary collaboration, between individuals with a background in business and individuals with a background in a particular technical or professional field related to the relevant opportunity, is emphasized. Projects can originate in an independent business idea or in collaboration with companies that partner with the course. In both cases, the emphasis will be on product or service develepment, built on technical or professional expertise, where the business case of the opportunity and its verification is in the foreground.

Field Course in Innovation and Entrepreneurship (I) (IÐN222F)

The course is taught in two parts, and the expectation is that students register for both parts. The course will cover the practical issues related to innovation and entrepreneurship. It covers the emergence of a business idea and the initial evaluation of the business opportunity, and the development and testing of a business model. This part of the course consists of lectures and case discussions that deal with various aspects of innovation and entrepreneurship: Analysis of business opportunities, evaluation of market size and unit contribution, the management of organizational units that are involved in innovation, financing, and other issues. Students will also tackle projects where they apply the methods taught in the class to isolated tasks in product and business development in both new and existing firms.

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

The AI lifecycle (REI603M)

In this course, we study the AI lifecycle, i.e. the productionisation of AI methods.
We explore the following parts of the lifecycle:
- Data collection and preparation
- Feature engineering
- Model training
- Model evaluation
- Model deployment
- Model serving
- Model monitoring
- Model maintenance
Three large projects will be handed out during the semester where students compete to solve AI problems.

Seminar on Machine Learning (TÖL028M)

In this course, students familiarize themselves with a particular topic in artificial intelligence (e.g. computer vision, natural language processing, data processing, generative modeling or other topics) by studying relevant academic literature and preparing a talk on this topic for their classmates.

Students can choose from a selection of topics provided by the teacher, or propose a topic that they are interested in on their own.

Besides learning about the subject matter of the talks, the goal of the course is to practice presentation skills.

The course starts on June 8th and will finish on August 17th. 

Compilers (TÖL202M)

The design of programming languages. The structure and design of compilers. Lexical analysis. Top down and bottom up parsing. Code generation. Each student writes his or her own compiler.

From an idea to reality II (TÖL211F)

The course is a continuation of the course "From an idea to reality". Students are expected to continue to develop their idea that they had in the "From an Idea to Reality" course. If the students are not satisfied with their previous idea and think they have come up with a better idea, that's fine, and if the teacher is satisfied with the new idea the student can continue with the new idea.

Students must apply for grants in the competition fund. Practice pitching your idea to potential investors and your fellow students.

The teacher will cover accounting and planning based on probability.

The teacher will cover company formation and tax matters.

Reasoned Programming (TÖL212M)

Fundamental concepts in program verification and reasoned programming are covered. Emphasis is placed on using reasoned programming to develop solid and proved versions of well-known algorithms, especially in the areas of searching, sorting, and binary search trees. Among algorithms covered are various versions of insertion sort, selection sort, quicksort, binary search, and searching in binary search trees. Emphasis will be placed on deepening the understanding of the algorithms as well as getting a good grip on program verification. Some exercises will use verification tools such as Dafny or OpenJML.

Final project (TÖL431L)

• 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.

Seminar in computer science (TÖL606M)

Students in computer science, software engineering and computational engineering attend weekly seminars where they present talks on their research projects or other related topics of interest.

Applied Cryptography (TÖL213M)

In this course, students will take a practical approach to understanding the building and breaking of cryptographic implementations. The first half of this module will involve programming tasks while the second half will involve two mini projects. 

Starting from simplistic approaches used historically, such as the Caeser cipher, to modern day protocols used to secure telecommunications ubiquitously, such as AES and TLS. Students will also consider the role of cryptography in society more broadly by researching how contentious technologies such as end-to-end encryption and Tor impact justice and privacy, and distributed ledgers can enable the decentralisation of key societal systems. Finally, the course will conclude with a mini project demonstrating a practical attack against a cryptographic implementation using ethical hacking approaches e.g. WiFi cracking, password bruteforcing or man-in-the-middle attack of an encrypted connection to sniff traffic. 

The course will encompass the following stages: 

1) Building cryptographic primitives in a programming language of choice. 

2) Application engineering best practices. 

3) A research project in cryptography and society. 

4) A practical project in ethical hacking approaches to demonstrating weaknesses in cryptography implementations. 

Quantum Computing and Quantum Cryptography (TÖL031M, TÖL030M)

Students will explore the fundamentals of quantum computing and its applications in computing and cryptography through hands-on programming exercises. Beginning with classical computing concepts, they will progress to quantum gates, quantum algorithms, and quantum cryptographic schemes such as quantum key distribution (QKD). Using tools like Qiskit, students will implement and simulate quantum circuits. Finally, students will complete a project that applies what they have learned to address a problem in quantum computing or quantum cryptography.

Topics covered:

• Introduction to classical and quantum computing
• Quantum mechanics fundamentals for computing 
• Quantum gates and circuits
• Quantum algorithms
• Introduction to quantum cryptography
• Error-correcting codes
• Quantum cryptography revisited
• Applications and impact of quantum computing and quantum cryptography

Quantum Cryptography (TÖL031M, TÖL030M)

In this course, students learn about quantum computing and, in particular, quantum cryptography. Quantum computing will cause a revolution in cryptography as many cryptosystems based on computational complexity will become obsolete. They will replaced by so-called quantum secure crypto systems.

Secure Software Engineering (HBV506M, HBV506M)

Secure software engineering involves identifying and mitigating vulnerabilities to reduce threats to an application. In this module, students will gain an understanding of secure engineering practices and the means to apply them throughout the complete software development life cycle.

Working in teams, students will design, develop, and maintain a web application for a customer following secure software engineering principles. Students will illustrate their understanding and practical competency of white box and black box security assessment through testing their own and other team’s applications for vulnerabilities against the OWASP top 10 most critical security risks to web applications.

It is therefore assumed the students have knowledge in web application development using JavaScript.

Secure Software Engineering (HBV506M, HBV506M)

Secure software engineering involves identifying and mitigating vulnerabilities to reduce threats to an application. In this module, students will gain an understanding of secure engineering practices and the means to apply them throughout the complete software development life cycle.

Working in teams, students will design, develop, and maintain a web application for a customer following secure software engineering principles. Students will illustrate their understanding and practical competency of white box and black box security assessment through testing their own and other team’s applications for vulnerabilities against the OWASP top 10 most critical security risks to web applications.

It is therefore assumed the students have knowledge in web application development using JavaScript.

Mentor in Sprettur (GKY001M)

In the course, the student's task consists in being a mentor for participants that are upper secondary school students and university students in the project "Sprettur". Mentors' main role is to support and encourage participants in their studies and social life. As well as creating a constructive relationship with the participants, being a positive role model, and participating in events organized in Sprettur. The mentor role centers around building relationships and spending meaningful time together with the commitment to support participants. 

Sprettur is a project that supports foreign students that would like to receive extra support to improve their performance and sustainability in their studies. The students in this course are mentors of the participants and are paired together based on a common field of interest. Mentors work in groups to support participants. Mentors plan activities with participants and spend three hours a month (from August to May) with Sprettur’s participants, three hours a month in a study group and attend five seminars that are spread over the school year. Students submit journal entries on Canvas once per semester and have to design and implement a learning experience for participants in Sprettur once in the course. Diary entries are based on reading material and students' reflections on the mentorship. Compulsory attendance in events, study groups, and seminars. The course is taught in Icelandic and English. 

Students fill out a digital application form and the teacher will contact them. 

More information about Sprettur can be found here: www.hi.is/sprettur  

Introduction to deep neural networks (TÖL506M)

In this course we cover deep neural networks and methods related to them. We study networks and methods for image, sound and text analysis. The focus will be on applications and students will present either a project or a recent paper in this field.

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.

The structure of Icelandic and language technology (MLT301F)

This course is intended for language technology students who do not have linguistic background. The purpose of the course is to give an overview of the structure of Icelandic, with a special consideration to features which can be problematic for natural language processing. The main topics that will be covered are the sound system of Icelandic and phonetic transcription (IPA and SAMPA); the inflectional and derivational morphology of Icelandic with a special consideration to Part-of-Speech tagging and tagsets; and the syntactic structure of Icelandic with emphasis on both phrase structure and dependency parsing.

Fundamentals of Ethical Hacking (TÖL605M)

Ethical hacking is the discipline of attacker perspective security analysis. Using tools and techniques used by malicious hackers, ethical hackers gain unique and unique and valuable insights used for improving system security.

This course will introduce students to the fundamentals of ethical hacking tools and techniques. Students will begin by configuring a secure lab using virtualisation tools. Then every two weeks a new stage of the ethical hacking methodology and some related tools will be introduced. The students will submit weekly lab reports evidencing their theoretical and practical understanding of the methodology and tools. 

For the best experience, students should own a PC able to run virtualisation software with an x86 image. Mac users with Apple CPU or those with PCs whose CPUs lack virtualisation support or who have limited RAM might have issues running a stable lab on their machine. Students who lack suitable hardware can use VMs on the department's cyber security server infrastructure.

Computational Intelligence (IÐN102M)

In designing intelligent systems, there is a need for autonomous systems that can learn to make good decisions from their own experience. This course introduces students to reinforcement learning—a computational approach in which systems automatically improve their performance through interaction and reward, without explicit instruction. The course covers Markov decision processes, dynamic programming, Monte Carlo methods, policy optimization, planning, search methods, and deep neural networks as function approximators. The curriculum combines theoretical discussion and project work, giving students the opportunity to model, analyze, and solve mathematical models for decision-making problems. Emphasis is also placed on the iterative process and on critical reflection regarding what it means for algorithms to learn and the impact that autonomous computational intelligence might have on science and society.

Project in language technology (TÖL019F)

Students work on a selected project in language technology with a supervisor.

Introduction to Information Security (TÖL029M)

This course provides the foundation for understanding the key issues associated with protecting information assets, determining the levels of protection and response to security incidents, and designing a consistent, reasonable information security system, with appropriate intrusion detection and reporting features.  The purpose of the course is to provide the student with an overview of the field of information security and assurance. Students will be exposed to the spectrum of security activities, methods, methodologies, and procedures.  Coverage will include inspection and protection of information assets, detection of and reaction to threats to information assets, and examination of pre- and post-incident procedures, technical and managerial responses and an overview of the information security planning and staffing functions.  Topics include risk assessment, authentication, securing web, application security, privacy and data protection, introduction to cryptography, security architecture, firewalls and other devices and network topologies.

Human Computer Interaction (TÖL502M)

Usually taught every second year.

The objective of the course is to allow students to examine more closely than is done in the introductory course Graphical User Interface Programming  HBV201G specific factors of HCI. The factors covered are user interface design using prototyping, programming of smart devices and users‘ acceptability of the software. There will be emphasis on different techniques and tools to develop prototypes. Also, on the design of user interfaces and native programming for smart phones or pads. The development process will be aimed at ensuring accessibility and acceptability of users. Students work on small projects individually, or on larger projects in groups. 

Final project (TÖL431L)

• 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.

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.

Programming Projects on Internet of Things (TÖL103M)

This course offers a hands-on introduction to programming small devices (microcontrollers, single-board computers) used in the context of Internet of Things, sensor networking, and home automation.

Such devices are often equipped with a large number of I/O pins, some RAM and flash memory, and wireless communication capabilities (e.g. WiFi and/or Bluetooth), making them attractive for tasks involving data acquisition,
processing, and wireless communication.

The course consists of bi-weekly programming tasks dealing with serial communication, data acquisition and analysis, programming of real-time O/S (RTOS), wireless communication and TCP/IP client-servers paradigm.

The biweekly assignments lead to a final project.

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.

Introduction to language technology (TÖL025M)

This course may be taught in English if requested.

In this course we will cover the basics of language technology with emphasis on the analysis and processing of written language. This includes the core subjects of language technology as well as their use in everyday life. We will present tools and corpora used within the field and specifically touch on how language technology is used for Icelandic.

The course emphasizes methods that make use of word embeddings, deep neural networks and language models for practical assignments and we will talk about how methods involving artificial intelligence have almost completely taken over the processing of human language. 

In this course there are 6 homework assignments that are worth 60% of the final grade as well as a larger final assignment that is worth 40%. For students that turn in a minimum amount of in-class assignments, the weight of the homework assignments will drop to 55%.

The AI lifecycle (REI603M)

In this course, we study the AI lifecycle, i.e. the productionisation of AI methods.
We explore the following parts of the lifecycle:
- Data collection and preparation
- Feature engineering
- Model training
- Model evaluation
- Model deployment
- Model serving
- Model monitoring
- Model maintenance
Three large projects will be handed out during the semester where students compete to solve AI problems.

The Linguistic System - Sounds and Words (ÍSL209G)

An introductory course in Icelandic phonetics, phonology, and morphology. The basics of acoustic phonetics and Icelandic articulatory phonetics will be introduced, accompanied by training in phonetic transcription. The main concepts of phonology will be presented, followed by an overview of sound alternations in Icelandic and their conditions. Basic concepts in morphology will be presented and the main word formation processes in Icelandic and their productivity will be dealt with. Grammatical categories in Icelandic will be outlined, the inflection of the main parts of speech will be described, and an overview given of inflectional classes and variations.

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

The AI lifecycle (REI603M)

In this course, we study the AI lifecycle, i.e. the productionisation of AI methods.
We explore the following parts of the lifecycle:
- Data collection and preparation
- Feature engineering
- Model training
- Model evaluation
- Model deployment
- Model serving
- Model monitoring
- Model maintenance
Three large projects will be handed out during the semester where students compete to solve AI problems.

Project in language technology (TÖL019F)

Students work on a selected project in language technology with a supervisor.

Seminar on Machine Learning (TÖL028M)

In this course, students familiarize themselves with a particular topic in artificial intelligence (e.g. computer vision, natural language processing, data processing, generative modeling or other topics) by studying relevant academic literature and preparing a talk on this topic for their classmates.

Students can choose from a selection of topics provided by the teacher, or propose a topic that they are interested in on their own.

Besides learning about the subject matter of the talks, the goal of the course is to practice presentation skills.

The course starts on June 8th and will finish on August 17th. 

Reasoned Programming (TÖL212M)

Fundamental concepts in program verification and reasoned programming are covered. Emphasis is placed on using reasoned programming to develop solid and proved versions of well-known algorithms, especially in the areas of searching, sorting, and binary search trees. Among algorithms covered are various versions of insertion sort, selection sort, quicksort, binary search, and searching in binary search trees. Emphasis will be placed on deepening the understanding of the algorithms as well as getting a good grip on program verification. Some exercises will use verification tools such as Dafny or OpenJML.

Final project (TÖL431L)

• 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.

Seminar in computer science (TÖL606M)

Students in computer science, software engineering and computational engineering attend weekly seminars where they present talks on their research projects or other related topics of interest.