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