Module Number

ML-4302
Module Title

Statistical Learning Theory
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Workload:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

Part 1: basic results in statistical learning theory:
• Statistical setup, estimation and approximation error, consistency
• Negative results: No free lunch theorem, slow rates of convergence
• Consistency of k nearest neighbor algorithms and partitioning algorithms
• Concentration inequalities
• Simple generalization bounds, for example with shattering coefficients and VC dimension
• Advanced generalization bounds, for example using Rademacher complexities, algorithmic stability, sample compression.
• Regularization and its consistency
Part 2: advanced results in statistical learning theory. This part of the lecture
changes, depending on the interests of the audience and the current state of
the art in the field and covers some of the recent results on learning theory. It
could cover topics like online learning, theory of unsupervised learning, theory
of deep learning, etc.

Objectives

Students get to know the standard tools and approaches in statistical learning
theory. They understand positive and negative results in learning theory, in
particular what are the fundamental limitations of machine learning, and which
properties are important to make a machine learning algorithm work.

Allocation of credits / grading
Type of Class
Status
SWS
Credits
Type of Exam
Exam duration
Evaluation
Calculation
of Module (%)
Lecture
V
o
2
3.0
wt
90
g
100
Tutorial
Ü
o
2
3.0
Prerequisite for participation There are no specific prerequisites.
Lecturer / Other von Luxburg
Literature

The literature for this lecture will be provided at the beginning of the semester. / Students need to know the contents of the basic math classes, in particular
linear algebra and probability theory.

Last offered -
Planned for -
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV