- 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
Machine Learning II - Statistical ML (M, 8.0 LP)
|Module Number||Module Name||CP (Effort)|
|INF-75-51-M-6||Machine Learning II - Statistical ML||8.0 CP (240 h)|
|CP, Effort||8.0 CP = 240 h|
|Position of the semester||1 Sem. in WiSe|
|Level|| Master (General)|
|Area of study||[INF-KI] Intelligent Systems|
|Reference course of study||[INF-88.79-SG] M.Sc. Computer Science|
|Type/SWS||Course Number||Title||Choice in |
|SL||SL is |
required for exa.
Machine Learning II - Statistical ML
|P||84 h||156 h||
- 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.
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
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)
Requirements for attendance (formal)
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) ||Elective Subjects||[W] Elective Module|
|[EIT-88.?-SG#2021] M.Sc. Embedded Computing Systems (ESY) ||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|