| Machine Learning [2022/23 WiSe] | ||
|---|---|---|
| Code IML |
Name Machine Learning |
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| LP 8 |
Dauer one semester |
Angebotsturnus in (irregular) alternation with *Fundamentals of Machine Learning* + *Advanced Machine Learning* |
| Format Lecture 4 SWS + Exercise course 2 SWS |
Arbeitsaufwand Arbeitsaufwand: 240h, thereof 60h lecture 90h tutorials, homework, lecture wrap-up 90h graded final report |
Verwendbarkeit cannot be combined with *Fundamentals of Machine Learning* or *Advanced Machine Learning* M.Sc. Angewandte Informatik M.Sc. Data and Computer Science M.Sc. Scientific Computing |
| Sprache English |
Lehrende Ullrich Köthe |
Prüfungsschema |
| Lernziele | Students understand a broad range of machine learning concepts, get to know established and advanced learning methods and algorithms, are able to apply them to real-world problems, and can objectively assess the quality of the results. In addition, students learn how to use Python-based machine learning software such as scikit-learn. |
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| Lerninhalte | This lecture is a compact version of the two-semester course *Fundamentals of Machine Learning* + *Advanced Machine Learning*: Classification (linear and quadratic discriminant analysis, neural networks, linear and kernelized support vector machines, decision trees and random forests), least squares and regularized regression, Gaussian processes, unsupervised learning (density estimation, cluster analysis, Gaussian mixture models and expectation maximization, principal component analysis, bilinear decompositions), directed probabilistic graphical models, optimization for machine learning, structured learning |
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| Teilnahme- voraus- setzungen |
recommended are: solid knowledge of basic calculus, statistics, and linear algebra | |
| Vergabe der LP und Modulendnote | The module is completed with a graded written examination. This examination is a report on a 90 h mini-research project. The final grade of the module is determined by the grade of the examination. The requirements for the assignment of credits follows the regulations in section modalities for examinations. Details will be given by the lecturer. | |
| Nützliche Literatur | Trevor Hastie, Robert Tibshirani, Jerome Friedman: The Elements of Statistical Learning (2nd edition), Springer, 2009; David Barber: Bayesian Reasoning and Machine Learning, Cambridge University Press, 2012 |
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