Machine Learning [2021 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 wrapup 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 
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 realworld problems, and can objectively assess the quality of the results. In addition, students learn how to use Pythonbased machine learning software such as scikitlearn. 

Inhalt  This lecture is a compact version of the twosemester 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 