The course consists of five days (Mon-Friday) of lectures and exercises on key topics in machine learning. The course (2.5 ects point) is passed by handing in a small report on one of the topics covered in the course. The course will cover key topics in machine learning including probabilistic multivariate modeling and Bayesian inference. The exercises cover both theoretical, technical programming and application aspects. It will be up to the students to decide on what aspects to focus on in the report. Specific machine learning application examples are used throughout the entire week. For further course details click here.
Technical University of Denmark, DTU Compute, building 421 room 73.
9AM-5PM every day August 25th-29th, 2014
Lectures will be given by invited speakers and staff at the Section for Cognitive Systems.
As preparation for the course we recommend reading the book by Christopher M. Bishop "Pattern Recognition and Machine Learning", Springer 2006 (chapter 1-6 and 9). The course requires basic Matlab programming skills.
Click here to download the course programme.
Mikkel N. Schmidt and Morten Mørup
Introduction to Bayesian Modeling
An introduction to Gausian Processes
Introduction to the analysis of learning algorithms: Does Bayesianism help?
Introduction to geometric statistics
Gaussian processes, expectation propagation and beyond
Marcel van Gerven
Machine learning for neural data analysis
Lars Kai Hansen
Quick and dirty estimation of latent variable models using moments: How quick and how dirty?
To register please send an email to Wanja Andersen email@example.com.
For academics there is no registration fee for the course. However, participants are to cover all other costs such as food, accommodation, and travel expenses. Due to space limitations we urge participants to register early.
For practical information regarding transportation and accommodation click here.
For further information, please contact: