Fundamentals of Machine Learning [2019 Sommer] | ||||
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CodeIFML |
NameFundamentals of Machine Learning |
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Leistungspunkte8 LP |
Dauerone semester |
Turnusin (irregular) alternation with *Machine Learning* |
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Lehrform4 SWS lecture (in English), 2 SWS tutorial, homework assignments |
Arbeitsaufwand240h; thereof 90h lectures and tutorials 90h lecture wrap-up and homework 60h graded final report |
Verwendbarkeitcannot be combined with *Machine Learning* B.Sc. Angewandte Informatik, M.Sc. Angewandte Informatik, M.Sc. Scientific Computing |
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Lernziel |
Students understand fundamental concepts of machine learning (features vs. response, unsupervised vs. supervised training, regression vs. classification etc.), get to know established 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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Inhalt |
The lecture, along with its sibling *Advanced Machine Learning*, offers an extended version of the one-semester course *Machine Learning*, with more room for regression methods, unsupervised learning and algorithmic details: Classification (nearest neighbor rules, linear and quadratic discriminant analysis, logistic regression, classical and randomized decision trees, support vector machines, ensemble methods); regression (linear and non-linear least squares, regularized and sparse regression, robust regression); unsupervised learning (hierarchical clustering, k-means algorithm, Gaussian mixture models and expectation maximization, principal component analysis, non-linear dimension reduction); evaluation (risk minimization, model selection, cross-validation) |
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Voraussetzungen |
recommended are: solid knowledge of basic calculus, statistics, and linear algebra | |||

Prüfungsmodalitäten |
cannot be combined with *Machine Learning*, written exam (report on a 60h mini-research project) | |||

Literatur |
Trevor Hastie, Robert Tibshirani, Jerome Friedman: The Elements of Statistical Learning (2nd edition), Springer, 2009 |