Machine Learning [2022 Sommer]  

Code IML 
Name Machine Learning 

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 wrapup 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 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. 

Lerninhalte  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 

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 exam. This exam is a report on a 90 h miniresearch project. The final grade of the module is determined by the grade of the exam. The requirements for the assignment of credits follows the regulations in section modalities for exams. 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 