Welcome to WASP Summer School on Machine Learning 2017.
KTH 7-11 of August
All lectures will be in room Q31 on Osquldas väg 6B.
Tuesday, 8th of August
09.00-12 Statistical relational learning and probabilistic programming part I, Luc De Raedt and Anton Dries, KU Leuven, Belgium [PDF, practical session 1]
13.00-18 Statistical relational learning and probabilistic programming part II, Luc De Raedt and Anton Dries, KU Leuven, Belgium [PDF, practical session 2, practical session 3, practical session 4, practical session 5 , solutions]
The concepts will be illustrated on a wide variety of tasks, including models representing Bayesian networks, probabilistic graphs, stochastic grammars, etc. This should allow participants to start writing their own probabilistic programs. We further provide an overview of the different inference mechanisms developed in the field, and discuss their suitability for the different concepts. We also touch upon approaches to learn the parameters of probabilistic programs, and mention a number of applications in areas such as robotics, vision, natural language processing, web mining, and bioinformatics.
Wednesday, 9th of August
09.00-12 Gaussian Processes part I, Marc Deisenroth, Imperial College London, UK [GP slides]
13.00-18 Gaussian Processes part II, Marc Deisenroth, Imperial College London, UK [BO slides, tutorial, research slides]
Thursday, 10th of August
09.00-12 Deep Learning part I, Josephine Sullivan, KTH [intro, classification, gradient, layered, CNN, tutorial (Python-files are in our Box)]
13.00-18 Deep Learning part II, Josephine Sullivan, KTH
More information can be found on the KTH Deep Learning Course web page.
Friday, 11th of August
09.00-12 Student research presentations
- Bayesian Frequency Tracking – How To Approximate, and How Not To, Martin Lindfors, LiU
Combining Machine Learning with Invariants Assurance Techniques for Autonomous Systems, Piergiuseppe Mallozzi, Chalmers
- Denoising Autoencoders, Vidit Saxena, Ericsson/KTH
- Olov Andersson, LiU