Chalmers Machine Learning Summer School

Machine learning has increasing importance in today's society, with ever-wider application of autonomous learning systems in areas ranging from advertising to energy and finance. It also has many scientific applications, as evidence by the growth of data science as a discipline. This summer school will give thorough introductions to a number of techniques and application areas in machine learning, including.

On the theory side, topics covered will include Bayesian inference, Deep learning, Gaussian processes, Markov decision processes, Monte-Carlo methods and Reinforcement learning. The applications will include Computational Biology, Computer vision, Energy and Smart Grids, Medicine and Robotics.

ECTS credits

If you have registered for the summer school, you can obtain up to 4 ECTS credits by completing the assignments. 

Please go to Piazza for more information on the assingments

Homework 1: Gaussian processes (0.5 credits)
Homework 2: Sequential Monte Carlo (1 credit)
Homework 3: Experiment design and Markov decision processes (1 credit) 
Homework 4: Conformal prediction (0.5 credits)

You must complete at least one assignment to get credits.


April 14-16, 2015


Fakultetsvåningen, Chalmers' Faculty Building


April 14: Large-scale inference and decision making

April 15: Learning in biology and medicine

April 16: Sequential Inference and Decision making

Organisation and sponsors


  • Christos Dimitrakakis
  • Devdatt Dubhashi


Published: Mon 02 Feb 2015. Modified: Tue 29 Sep 2015