Enkeltfag på kandidatniveau

This course introduces students to cutting-edge robotics research and hands-on applications. Over several multi-week modules, students will work with modern robotic systems, learning both theory and practical implementation. Each module covers a specific area of robotics research, combining lectures with hands-on projects that let students apply what they've learned. The course emphasizes practical problem-solving and real-world applications of robotics technology.

In today's world of professional software development, ensuring your code works perfectly is not just a final step--it is an ongoing process. As systems become more complex, developers need more than just intuition and manual checks. They need systematic ways to test and analyze programs to catch bugs and validate behavior. This course is all about giving you the tools, techniques and hands-on experience to just do that.

In this course, we teach advanced machine learning (ML) techniques and hands-on skills for applying these techniques to natural language processing (NLP) problems.

This course introduces advanced machine learning techniques with a focus on the visual domain, emphasizing their role in both research and real-world applications. Students will gain practical skills through hands-on exercises to support theoretical understanding. We aim to enabling them to choose and adapt appropriate methods for specific tasks. In the age of generative AI, understanding the underlying principles is essential. The course contributes to developing AI literacy by introducing foundational concepts, diving into selected technical details, and fostering critical discussions around the capabilities and limitations of current approaches.

In this course, you will learn different aspects of modern data systems, in particular state-of-the-art data mangement techniques that have been used or proposed in industry and academia.

Learn how to analyze software systems to ensure they meet security policies under specific trust assumptions. This course covers topics such as information flow, declassification, non-interference, timing attacks, and program analysis tools, with a focus on reasoning about program behavior and securing data flows.

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 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.

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 computer systems 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 at the lowest level 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 like game engines that allow the development and sharing 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 course aims to teach the understanding, design, implementation, and use of state-of-the-art artificial intelligence (AI) techniques applied to games and other simulation environments. We mainly cover methods to control agents (NPC, bots, robots, etc.), focusing on neural network-based approaches but also tree search techniques such as MCTS. Additionally, this course covers generative AI methods and their use for environment/game level generation, in addition to exploring the use of Large Language Models for games.