Again this year, we have our Summer Ph.D. Course in Advanced Signal Processing (02901).
The course will cover probabilistic multivariate modeling and Bayesian inference, convex optimization, low rank approximations and kernel methods.
Lecturers: Yee Whye Teh (University College London), Ryota Tomioka (University of Tokyo), Ulrich Paquet (Microsoft Research Cambridge), plus Mikkel N. Schmidt, Morten Mørup, Ole Winther, and Lars Kai Hansen (Cognitive Systems, DTU Informatics).
Course description: 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 probabilistic multivariate modeling and Bayesian inference, convex optimization, low rank approximations and kernel methods. 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.
Yee Whye Teh
Convex optimization: old tricks for new problems
Mikkel N. Schmidt
Introduction to Bayesian inference
Mining Graphs by Relational Modeling
Lars Kai Hansen
Learning from small samples in high dimensions
Information and registration:
Please see http://imm.dtu.dk/courses/02901
for more information about the course and registration.