Enkeltfag på kandidatniveau

In this course, we teach both advanced machine learning (ML) approaches and hands-on skills for applying these approaches to data science problems.

This course will introduce classes of tasks that are at the core of most real-world production systems. It will teach advanced solutions to solve these tasks on complex and large-scale data with state-of-the-art tools.

This course provides an overview on the topics of bias and fairness in data, models, and algorithms, and on the associated ethical and accountability issues.

In this course, you will learn how to analyze and improve the performance of a computer system with a specific focus on data-intensive systems.

To investigate a system’s performance and come up with solutions to improve it, there are three key things to know.

1. How to devise a methodology to design experiments so that you can investigate the system’s performance,
2. The toolset to run the necessary experiments and collect the results, and
3. At least a basic understanding of the systems layers (hardware, operating systems, etc.) to be able to interpret your results and come up with solutions.

This is what we will cover in this course.

In this course, students learn how to work in data-driven design and development processes and what changes data analysis brings to the design and maintenance of games.

The course will introduce and cover basic concepts in Enterprise Architecture -an approach to understand, manage and develop business strategy in a complex IT landscape. There will be an introduction to the recognised frameworks and models in Enterprise Architecture.

This course aims to enable students to design and develop websites and interactive Web applications based on standard Web technologies and software frameworks.

The subject of the course is functional, declarative programming in general and F# in particular.

This course gives an introduction to IoT as well as practical experience with the design and implementation of end-to-end IoT systems.

Practical examples and exercises will predominatly be from domains Energy and Environment.

The course "DevOps, Software Evolution and Software Maintenance" is an MSc elective.

In this course, the students will discover all the software engineering activities that take place after an initial software product is delivered or after a legacy system is taken over from a theoretical and practical perspective. Students (in groups) will take over such a system that is live and serving users, they will refactor and migrate it to the languages and technologies of their liking. All subsequent DevOps, software evolution and software maintenance activities will be performed directly on the systems of the students.

This course is designed to give students a basic introduction to programming and data processing. Students will get a hands-on introduction to the Python programming language and how to work with tabular data. This will enable them to solve simple programmatic tasks as well as to get an idea of what the role of the programmer entails. After the course student will be able to discuss and reflect on challenges and opportunities derived from approaching the world through algorithms.

Algorithmic problem solving is the art of designing algorithms to solve challenging programming problems. This requires extensive practice in classifying problems, choosing the right data structures, and identifying suitable algorithms. This is very different to a standard algorithm and data structure course, where the focus is on understanding the tools at hand, but not on using them to solve problems. This course bridges the gap between theoretical knowledge and practical solutions to computational problems.

This course provides an introduction to the fundamental concepts of cryptography, the practice of protecting information and securing communication in computer systems. We start with classical encryption techniques, and consider modern private-key and public-key crypto systems and study their security against eavesdroppers or attackers, who tamper with network traffic. The course is aimed at students with a background in computer science or mathematics, and will include both theoretical and programming exercises. 

This course offers a comprehensive exploration of service design and management, equipping students with essential processes, principles, methods, and tools. It focuses on the strategic design and management of services, including digital components, and draws on expertise from various disciplines within service design and management. Through lectures, in-depth case studies, and interactive hands-on exercises, students will examine the characteristics and dynamics of services and service ecosystems. The course aims to deepen students' understanding of how services can be effectively designed, managed, and implemented to deliver substantial value to users, providers, and other stakeholders.

This course examines organizations in light of contemporary challenges with digitalization. It explores organizational perspectives on digitalization and cover theories that can help us better understand, and critically reflect upon, such profound challenges, while also offering hands-on approaches to managing them. Furthermore, it investigates questions of the changing nature of work, and themes such as leadership, management, and organizing in order to make students both knowledgeable about, and skilled in the practice of, and engaging with, digital transformation.

The goal of this course is to equip students to understand how underlying technological and social infrastructures shape the digital economy.

This course will introduce students to techniques for solving complex programming tasks arising in modern IT systems. The focus in the course is on algorithm design and analysis.

This course addresses advanced programming techniques, with a special attention on functional programming and its applications. The course is a perfect balance of theory and practice, with focus on the Scala programming language.

The course is designed to enable the student to engage with future developments in software engineering by providing a forum for the critical discussion of processes, methods, tools, and architectures. By combining theoretical insights from state-of-the-art research and hands-on experience with advanced tools and techniques, students gain a comprehensive understanding of software engineering.

This course is an advanced course on algorithms which builds on top of an introductory course on algorithms and data structures. The course focuses on advanced techniques for identifying and solving computationally hard problems and on how to adapt such techniques to real-world scenarios.

This course is about that part of programming that focuses on parallelism and concurrency. It covers concurrency concepts that are universal to any programming language. The Java programming language is the main language used for practically addressing such aspects; and Erlang is also used for a topic in the course.

The course will teach students to analyse complexity within an empirical case that explores a current topic within the fields of science, technology, and innovation.

This course gives an introduction to the field of data mining. The course is relatively practically oriented, focusing on applicable algorithms. Practical exercises will involve both use of a freely available data mining package and individual implementation of algorithms.

In this course students will learn to apply modern state-of-the-art solutions for natural language processing problems. We go beyond simple classification tasks, and tackle more advanced types of tasks, like generation and structured prediction. 

Networks are all around us: We are ourselves, as individuals, the units of a network of social relationships of different kinds; the Internet and the highway system can be modelled as networks embedded in space; networks can be also entities defined in an abstract space, such as networks of acquaintances or collaborations between individuals. This course aims at providing the computational tools to study these networks and form an advanced understanding of the current state of the art of network science. The final objective is to have the students master computational techniques to solve advanced network problems, to be able to contribute to the development of network analysis, and to appreciate the limitations and future developments of scientific papers dealing with network problems in real-world data.

This hands-on course teaches you how the design, implementation, and implications, of systems software influence your task as an application programmer. You will get an in-depth understanding of how hardware and the operating system work; in the process, you will learn how computers actually work, and what actually happens when you run an application. This enables you to fully exploit underlying hardware and systems software to write high-quality software.

Today we rarely use programs that are developed to fit one specific customer. When we play games, use business systems for administration, or download apps to our phones, we use software that is part of software ecosystems: software products that are configured to fit a specific business context; platforms that allow sharing of specific kinds of software; or software as a service. Examples of software ecosystems have developed around software products, like game engines, or enterprise resource planning (ERP or CRM) software, integrated development environments like visual studio and platforms like the Android operating system. 

In this course you explore software ecosystems from UX design, organizational and technical perspectives. To this end you will analyse a software ecosystem together, propose an improvement related to the ecosystem, and support your proposal with a prototype and / or other design artefacts.

This course enables students to understand the effects of automated digital systems on the way a welfare society is governed and experienced by social groups and individuals.

The course aims at giving the students an introduction to the complexities of determining how processes of digitalisation and green transitions are entangled.

Students attending this course will study how to design game code that is scalable and takes into account hardware and software resources.

This course examines organizations in light of contemporary challenges with digitalization. It explores organizational perspectives on digitalization and cover theories that can help us better understand, and critically reflect upon, such profound challenges, while also offering hands-on approaches to managing them. Furthermore, it investigates questions of the changing nature of work, and themes such as leadership, management, and organizing in order to make students both knowledgeable about, and skilled in the practice of, and engaging with, digital transformation.

The goal of the course is to teach the understanding, design, implementation, and use of modern artificial intelligence (AI) techniques for generating efficient, intelligent behaviors in games and other simulation environments. A particular focus is given to techniques applied to agent-based simulations.

The course presents the students with several perspectives on game analysis and design. The unique aspect of the course is the combination of theoretical and practical approaches. Every unit familiarizes the students with the newest development in game studies and follows it with a presentation by a practitioner. This shows the students the interplay between the academic and industry perspectives. The students must also use the topics raised during the lectures as inspiration for their game creation or their original analyses of game elements.