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


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 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.
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
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 mini-research 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