Computer Science

This course provides an integrated treatment of two critical topics for a computing professional: human computer interaction (HCI) and security. It will cover basic skills to evaluate systems for their effectiveness in meeting people's needs within the contexts of their use, building knowledge of common mistakes in systems, and approaches to avoid those mistakes.

This course examines the field of machine learning, focusing on the core concepts of supervised and unsupervised learning. In supervised learning, we will discuss algorithms that are trained on input data labelled with the desired output.  Examples of these topics include decision trees, regression, support vector machines, and neural networks. In unsupervised learning, we aim to discover latent structure from input data where no output labels are available. Examples of these topics include clustering and association rule mining.  Students will learn fundamental theory and algorithms that underpin these machine learning techniques, as well as develop an understanding of the relationships between these algorithms and their practical implementation. We will discuss practicalities in the application of machine learning to a range of problems.

This course provides a rigorous introduction to programming, abstraction, and the structure of programs. It is designed for students who wish to deepen their understanding of computer science concepts in the context of data science. The primary programming language used is Python; however, the course emphasizes foundational programming ideas, not just language-specific syntax.

It is recommended, but not strictly required, that students have taken a Foundations of Data Science course. There is no formal programming-related prerequisite for this course; students do not need to be familiar with any particular programming language before starting the course.

Topics covered in this course include writing and debugging software for hardware, working with microcontrollers, sensors, motors, key theoretical concepts for robotic systems, the scientific method applied to robotic systems, and scientific reporting and writing.

This course introduces a variety of basic principles, techniques, and modern advances in text mining. Topics to be covered include basic natural language processing techniques, text representation, text classification, feature selection, text clustering and topic models, word embedding, deep neural networks and introduction to (large) language models. During the course, students actively learn how to apply text mining methods to data analysis and how to use them together with natural language processing and machine learning techniques on real data problems. The course has a strong practical focus: students gain hands-on experience in Python by applying the methods to real data during the course and interpreting the results. The course provides an understanding of the principles, problems, techniques, and solutions associated with text mining and to enable them to gain knowledge of how recent advances in text mining relate to innovative approaches to organizing, characterizing, finding and exploiting large amounts of textual information in the search for new knowledge. Assumed knowledge includes basic knowledge/motivation in programming and data science.

The course providers the knowledge, skills, and experience from taking part in an industrially based mechatronic development project, which is conducted up to a working prototype. The principal design of the product has been formed in the course Applied Mechatronics. It is essential that the work is done in a team with competences from various fields. The project is done during two study periods. The course participants should develop the mechatronic parts of those projects or other purely mechatronic products. The development process starts with extensive information search, brainstorming, and evaluation, activities which often encompass 30-40% of the total workload. This has been done in the course EIEN65 Applied Mechatronics. Then follows in this course selection of concept, constructive design of the product idea, ordering of components, building, testing, and adjustments. The course concludes with the official presentation of the designed products, where representatives from industry, course leaders, and the press take part. Assumed prior knowledge: Applied Mechatronics.

This course examines the theory and practice of machine learning, including conceptual, computational, mathematical and statistical frameworks. Topics include: classification, regression, optimization, neural networks, deep learning, unsupervised learning, semi-supervised learning, clustering, dimensionality reduction and generative models. The implementation of machine learning techniques, experimentation and practical application are a central theme of the course.

There are several languages for programming quantum protocols. Each has its own strengths and weaknesses. This course surveys current platforms (OpenQAsm, Qiskit, Q#, Quipper, Quantomatic, and PyZX) and analyzes their respective features semantically. The theoretical analysis uses category theory, a powerful mathematical tool in logic and informatics, that has influenced the design of many modern programming languages. It enables a powerful graphical calculus that lets you draw pictures instead of writing algebraic expressions. This technique is visually extremely insightful, yet completely rigorous. For example, correctness of protocols often comes down to whether a picture is connected or disconnected, whether there is information flow from one end to another. In a practical way, this course investigates the conceptual reasons why quantum protocols and quantum computing work, rather than their algorithmic and complexity-theoretic aspects.

In this course, students who are already familiar with the key theoretical foundations of artificial intelligence and machine learning dive deeper into the exciting capabilities of this area of research and its applications. The course begins with computer vision algorithms for classification, recognition, detection, and their implementation in deep learning libraries, before exploring autoencoders and variational autoencoders, and gaining insights into the training and application of generative adversarial networks. It then proceeds to an in-depth examination of diffusion models, including score-based diffusion models, latent diffusion models, and Stable Diffusion. The final part of the course explores even more advanced topics, including the representation of 3D objects, vision transformers, video classification, and text to image generation.

This course covers modern approaches to artificial intelligence with a focus on neural networks (NNs). Among other topics, students cover discussions of NN loss functions, optimization, and back propagation. Throughout the course there is a focus on students understanding theory and modelling principles in order to apply them effectively to AI. At the end of the course, students apply these principles to other AI tasks and learn how that can be done in practice.