Mathematical Foundations of Machine Learning
Module MA4801
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Module version of WS 2020/1 (current)
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
available module versions | ||
---|---|---|
WS 2020/1 | SS 2020 | WS 2015/6 |
Basic Information
MA4801 is a semester module in English language at Master’s level which is offered in summer semester.
This Module is included in the following catalogues within the study programs in physics.
- Specialization Modules in Elite-Master Program Theoretical and Mathematical Physics (TMP)
Total workload | Contact hours | Credits (ECTS) |
---|---|---|
180 h | 60 h | 6 CP |
Content, Learning Outcome and Preconditions
Content
(1) the perceptron
(2) network architecture (feedforward networks)
(3) Kolmogorov superposition theorem
(4) backpropagation and learning algorithms
(5) approximation properties of different architectures
B Kernel Methods
(1) positive definite kernels
(2) Mercer kernels
(3) reproducing kernel Hilbert spaces
(4) regularization techniques and support vector machines
(5) representer theorem for the minimizer
(6) numerical algorithms for SVM's
C Qualitative Theory
(1) loss functions
(2) risk functionals
(3) empirical risk minimization
(4) bias-variance dilemma
(5) consitency
(6) complexity bounds
Learning Outcome
Preconditions
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
Type | SWS | Title | Lecturer(s) | Dates | Links |
---|---|---|---|---|---|
VO | 2 | Mathematical Foundations of Machine Learning | Hasenöhrl, M. Wolf, M. |
Mon, 10:15–11:45, virtuell |
eLearning documents |
UE | 2 | Mathematical Foundations of Machine Learning (Exercise Session) | Hasenöhrl, M. Wolf, M. |
documents |
Learning and Teaching Methods
Media
- Blackboard
- Slides
Literature
D.J.C. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge Univ. Press 2003.
V.N. Vapnik, Statistical Learning Theory, Wiley 1998.
T. Hastie, R. Tibshirani, J. Fiedman, The Elements of Statistical Learning Theory, Springer 2009.
Module Exam
Description of exams and course work
Exam Repetition
The exam may be repeated at the end of the semester.