Courses in machine learning
Main content
Master students in Machine Learning have the compulsory听courses Introduction to Machine Learning INF264, Algorithms INF234, Deep Learning INF265, and Reinforcement Learning INF266 in addition to the听Master's thesis in Informatics INF399.
We recommend the following additional courses, depending on students interests:
- INF250 Foundations of Data-Oriented Visual Computing
- INF270 Introduction to Optimization Methods
- INF271 Combinatorial Optimization
- INF272 Nonlinear Optimization
- INF273 MetaHeuristics
- INF367 Selected topics in Artificial Intelligence (see below for topics)
- INF368听Selected topics in Machine Learning (see below for topics)
- Monte Carlo Methods and Bayesian Statistics
- Statistical Learning
- 听AI Ethics
听
The following courses are taught by the Machine Learning group at the Department of Informatics:
INF264 Introduction to Machine Learning (autumn)
Machine learning is a branch of artificial intelligence focusing on algorithms that enable computers to learn from and change behavior based on empirical data. The course gives an understanding of the theoretical basis for machine learning and a set of concrete algorithms including decision tree learning, artificial neural networks, Bayesian learning, and support vector machines. The course also includes programming and use of machine learning algorithms on real-world data sets.
Click here 鈫捥�INF264
INF265 Deep Learning (spring)
Artificial neural networks are flexible and powerful machine learning models. Modern deep learning has had tremendous success in applying complex neural networks to problems from a wide range of disciplines. This course gives and understanding of the theoretical basis underlying neural networks and deep learning. Furthermore, the course includes implementation of neural components and as well as applying deep learning on real-world data sets using modern deep learning packages.
Click here 鈫捥�INF265
INF266 Reinforcement Learning (spring)
Reinforcement learning is one the main paradigms of modern machine learning, artificial intelligence and robotics, with wide applications for decision-making and for the training of autonomous agents. This course provides an understanding of the foundation of reinforcement learning, analyzes classical reinforcement learning algorithms, and shows how practical problems can be modelled and solved with a reinforcement learning approach.
Click here 鈫捥�INF266
INF367 Selected topics in Artificial Intelligence
Fall 2025:听Diamonds and Rust in the AI Treasure Chest
Spring 2025: Applied Machine Learning
Fall 2024: Quantum Computing and Quantum Machine Learning
Fall 2023: Quantum Computing and Quantum Machine Learning
Spring 2023: Geometric deep learning
Fall 2022: Natural language processing
Spring 2022: Topological machine learning
Fall 2021: Ontologies and Knowledge Graphs
Spring 2021:听Machine learning and societal questions
Fall 2020: Learning Theory and Neuro-symbolic AI
Click here 鈫捥�INF367
INF368听Selected topics in Machine Learning
Spring 2024: Reinforcement learning
Spring 2023: Reinforcement learning
Fall 2022: Advanced deep learning
Spring 2022: Reinforcement learning
Fall 2021: Natural language processing
Spring 2021: Reinforcement learning
Spring 2020: Deep learning
Click here 鈫�听INF368
听