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Machine Learning and Applications

Instructor: Christoph Lampert

Teaching Assistant: Alex Kolesnikov, Alex Zimin


The course follows a hybrid lecture/seminar format. The first four to six meetings will consist of an introduction to machine learning in the form of classroom lectures. The remaining meetings will consist of presentations by the participants on selected applications of machine learning in particular disciplines.

Requirements/Exams: regular attendance, active participation, 60min oral presentation

Credits: 3

Final Grade: pass/fail

Schedule (subject to change)

Date Lecture Speaker Topic
Mar 3 Tue-
pre-meetingTopic Discussion and Assignments
Mar 5 Thu 1C. LampertA Practical Introduction
Mar 10 Tue 2 C. Lampert
Decision Theory, Generative Probabilistic Models
Mar 12 Thu 3 C. Lampert

Discriminative Models, Maximum Margin Classifiers

Mar 17 Tue 4 C. Lampert
Statistical Learning Theory
Mar 19 Thu 5 C. Lampert Unsupervised Learning, Bayesian Networks
Mar 24 Tue6C. Domokos
Undirected Graphical Models
Mar 26 Thu7T. TarrachProbabilistic Programming
Mar 31 Tue-spring break
Apr 2 Thu-spring break
Apr 7 Tue- spring break
Apr 9 Thu -spring break
Apr 14 Tue 8P. Daca
Reinforcement Learning for MDPs
Apr 16 Thu 9C. KamathLearning with Errors
Apr 21 Tue 10A. FellnerAutomated Testing/Verification

Apr 23 Thu

-no meeting

Apr 28 Tue

11R. ZhangShape Classification
Apr 30 Thu12B. KraglMachine Learning for Theorem Proving