Module Handbook

  • Dynamischer Default-Fachbereich geändert auf INF

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

  • 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

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