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Machine learning courses

Courses in machine learning

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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.

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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.

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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

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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

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