Module Handbook

  • Dynamischer Default-Fachbereich geändert auf MAT

Course MAT-62-21-K-7

Statistical Learning and Selected Applications (2V, 4.5 LP)

Course Type

SWS Type Course Form CP (Effort) Presence-Time / Self-Study
2 V Lecture 4.5 CP 28 h 107 h
(2V) 4.5 CP 28 h 107 h

Basedata

SWS 2V
CP, Effort 4.5 CP = 135 h
Position of the semester 1 Sem. irreg.
Level [7] Master (Advanced)
Language [EN] English
Lecturers
Area of study [MAT-STO] Stochastics/Statistics/Financial Mathematics
Livecycle-State [NORM] Active

Contents

  • Basic concepts of machine/statistical learning,
  • Bayesian model choice,
  • Local models,
  • Random Forests, increased efficiency through combination of several experts, boosting,
  • Support Vector Machines,
  • Gaussian processes for regression and classification,
  • Neural Networks,
  • Identification of dynamic systems,
  • Selected application examples:
    • Identification of vehicle systems for (load) prediction (e.g. predictive maintenance) and control (e.g. forcontrol units or driver assistance systems)
    • Classification of working conditions based on easy to measure machine data,
    • Data-driven driver and operator modeling, machine learning for autonomous driving,
    • Reinforcement Learning for the control of traffic systems and in robotics.

Literature

  • R. A. Berk: Statistical Learning from a Regression Perspective,
  • C. Bishop: Pattern Recognition and Machine Learning,
  • K. Murphy: Machine Learning: A Probabilistic Perspective,
  • J. Franke: Lecture Notes on Nonparametric Statistics.

Materials

Further literature will be announced in the lecture.

Requirements for attendance (informal)

Classical regression analysis, e.g. from the module [MAT-60-12-M-4] Regression and Time Series Analysis.

Modules:

Requirements for attendance (formal)

None

References to Course [MAT-62-21-K-7]

Module Name Context
[MAT-62-21-M-7] Statistical Learning and Selected Applications P: Obligatory 2V, 4.5 LP