[IML] - [de] - [Machine Learning]


Machine Learning [2018 Sommer]
Code
IML
Name
Machine Learning
Leistungspunkte
8 LP
Dauer
one semester
Turnus
in (irregular) alternation with *Fundamentals of Machine Learning* + *Advanced Machine Learning*
Lehrform
4 SWS lecture (in English), 2 SWS tutorial, homework assignments
Arbeitsaufwand
240h; thereof
90h lectures and tutorials
120h lecture wrap-up and homework
30h preparation for examination
Verwendbarkeit
cannot be combined with *Fundamentals of Machine Learning* or *Advanced Machine Learning*
B.Sc. Angewandte Informatik,
M.Sc. Angewandte Informatik,
M.Sc. Scientific Computing,
M.Sc. Physik (specialization Computational Physics)
Lernziel 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.
Inhalt 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
Voraussetzungen recommended are: solid knowledge of basic calculus, statistics, and linear algebra
Prüfungs
modalitäten
cannot be combined with *Fundamentals of Machine Learning* or *Advanced Machine Learning*, successful homework solutions (at least 50% of total achievable points) and oral examination
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