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Algorithms for Machine Learning and Inference

Master's level | 7.5 credits | Course code: DIT381

The course is not given as a stand-alone course this academic year, but can be included in the program/course package. For information contact the department.

About the Course

This course will discuss the theory and application of algorithms for machine learning and inference, from an AI perspective. In this context, we consider as learning to draw conclusions from given data or experience which results in some model that generalises these data. Inference is to compute the desired answers or actions based on the model.

Algorithms of this kind are commonly used in for example classification tasks (e.g., character recognition, or to predict if a new customer is creditworthy) and in expert systems (e.g., for medical diagnosis). A new and commercially important area of application is data mining, where the algorithms are used to automatically detect interesting information and relations in large commercial or scientific databases.

The course intends to give a good understanding of this crossdisciplinary area, with a sufficient depth to use and evaluate the available methods, and to understand the scientific literature. During the course we may discuss potential problems with machine learning methods, for example, bias in training data and safety of autonomous agents. 

The following concepts are covered:

  • Bayesian learning: likelihood, prior, posterior
  • Supervised learning: Bayes classifier, Logistic Regression, Deep Learning, Support Vector Machines
  • Unsupervised learning: Clustering algorithms, EM algorithm, Mixture models, Kernel methods
  • Hidden Markov models, MCMC
  • Reinforcement learning

More Information

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Course Syllabus


Tuition Fee

Please contact the department.
EU/EEA citizens, Swedish residence permit holders and exchange students do not pay fees. More information on:

Study Guidance



Department of Computer Science and Engineering
41296 Göteborg

Visiting address: Rännvägen 6 B

Page Manager: Jörgen Ölund
Last update: 2/18/2020 2:41 PM

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Page Manager: Jörgen Ölund|Last update: 10/16/2018

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