- Introduction and Overview
- Statistical machine learning
- Frequentist approach
- Bayesian approach
- Discriminative vs. generative Models
- Graphical models and topic models
- Gaussian processes
- Deep generative models: VAEs and GANs
- Reinforcement Learning
- Selected advanced topics
Module INF-75-51-M-6
Machine Learning II - Statistical ML (M, 8.0 LP)
Module Identification
Module Number | Module Name | CP (Effort) |
---|---|---|
INF-75-51-M-6 | Machine Learning II - Statistical ML | 8.0 CP (240 h) |
Basedata
CP, Effort | 8.0 CP = 240 h |
---|---|
Position of the semester | 1 Sem. in WiSe |
Level | [6] Master (General) |
Language | [EN] English |
Module Manager | |
Lecturers | |
Area of study | [INF-KI] Intelligent Systems |
Reference course of study | [INF-88.79-SG] M.Sc. Computer Science |
Livecycle-State | [NORM] Active |
Courses
Type/SWS | Course Number | Title | Choice in Module-Part | Presence-Time / Self-Study | SL | SL is required for exa. | PL | CP | Sem. | |
---|---|---|---|---|---|---|---|---|---|---|
4V+2U | INF-75-51-K-6 | Machine Learning II - Statistical ML
| P | 84 h | 156 h |
U-Schein
| ja | PL1 | 8.0 | WiSe |
- About [INF-75-51-K-6]: Title: "Machine Learning II - Statistical ML"; Presence-Time: 84 h; Self-Study: 156 h
- About [INF-75-51-K-6]: The study achievement [U-Schein] proof of successful participation in the exercise classes (ungraded) must be obtained. It is a prerequisite for the examination for PL1.
Examination achievement PL1
- Form of examination: written exam (Klausur) (60-180 Min.)
- Examination Frequency: each winter semester
- Examination number: 67551 ("Machine Learning II")
Evaluation of grades
The grade of the module examination is also the module grade.
Contents
Competencies / intended learning achievements
After successfully completing the module, students will be able to:
- solve complex ML problems using advanced probabilistic methods
- understand the various theoretical concepts and motivations behind different statistical learning paradigms
- formalize a problem in terms of a probabilistic model and the derive respective learning and inference algorithm
- explain numerical approaches to learning (optimization and integration) and how they relate to the Bayesian and frequentist approach
Literature
Relevant literature (in order of descending importance):
- Bishop, C. "Pattern Recognition and Machine Learning (Information Science and Statistics), 1st edn. 2006. corr. 2nd printing edn." Springer, New York(2007).
- Richard Sutton and Andrew Barto. Reinforcement Learning. Second Edition. MIT Press, 2018.
- Jerome Friedman, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning. Vol. 1. Springer, Berlin: Springer series in statistics, 2001.
Requirements for attendance (informal)
None
Requirements for attendance (formal)
None
References to Module / Module Number [INF-75-51-M-6]
Course of Study | Section | Choice/Obligation |
---|---|---|
[EIT-88.A20-SG#2021] M.Sc. European Master in Embedded Computing Systems (EMECS) [2021] | Elective Subjects | [W] Elective Module |
[EIT-88.?-SG#2021] M.Sc. Embedded Computing Systems (ESY) [2021] | Elective Subjects | [W] Elective Module |
[INF-88.79-SG] M.Sc. Computer Science | Specialization 1 | [WP] Compulsory Elective |
[MAT-88.105-SG] M.Sc. Mathematics | Subsidiary Topic (Minor) | [WP] Compulsory Elective |