Machine Learning
Machine learning courses

Courses in machine learning

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Master students in Machine Learning have the compulsory courses Introduction to Machine Learning INF264, Algorithms INF234, and Deep Learning INF265 in addition to the 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 Selected topics in Machine Learning (see below for topics)
  • STAT250 Monte Carlo Methods and Bayesian Statistics
  • STAT260 Statistical Learning
  • AIKI210 AI Ethics


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 → 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 → INF265

INF367 Selected topics in Artificial Intelligence

Fall 2020: Learning Theory and Neuro-symbolic AI

Spring 2021: Machine learning and societal questions

Fall 2021: Ontologies and Knowledge Graphs

Spring 2022: Topological machine learning

Fall 2022: Natural language processing

Spring 2023: Geometric deep learning

Fall 2023: Quantum Computing and Quantum Machine Learning

Fall 2024: Quantum Computing and Quantum Machine Learning

Click here → INF367

INF368 Selected topics in Machine Learning

Spring 2020: Deep learning

Spring 2021: Reinforcement learning

Fall 2021: Natural language processing

Spring 2022: Reinforcement learning

Fall 2022: Advanced deep learning

Spring 2023: Reinforcement learning

Spring 2024: Reinforcement learning

Click here → INF368