Lecture: Machine Learning II, Winter Term 2016/17

Professor Marius Kloft
Dr. Patrick Jähnichen
Florian Wenzel

Topics:

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.

Dates

Lecture: Fridays, 11-13:15
Exercise: 13:30- 14:30

Requirement:

Profound knowledge of machine learning as taught in the course Machine Learning I. Basic knowledge of probability and statistics helpful.

Exam

There will be an oral exam. The exam dates are:

to be announced

Literature:

Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. Foundations of machine learning. MIT press, 2012.