Lecture: Machine Learning II, Winter Term 2016/17
Professor Marius Kloft
Dr. Patrick Jähnichen
- Probabilistic machine learning (Bayesian decision theory, linear models, GPs, mixture models, variational methods)
- Statistical learning theory (Concentration and Rademacher Analysis)
- Advanced methods (MC-SVMs, HMMs, structured prediction, random forests, MTL, MKL)
First lecture: Friday, 21 Oct 2016.
The lecture is accompanied by an exercise course. For questions regarding the exercise contact Florian Wenzel.
Both, lecture and exercise are managed via Moodle: [lecture], [exercise],
where homework is submitted and slides can be obtained using the password provided in the first lecture.
Lecture: Fridays, 11-13:15
Exercise: 13:30- 14:30
Profound knowledge of machine learning as taught in the course Machine Learning I. Basic knowledge of probability and statistics helpful.
There will be an oral exam.
The exam dates are:
- Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
- Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. Foundations of machine learning. MIT press, 2012.