Fundamentals of Machine Learning [2022 Sommer]  

Code IFML 
Name Fundamentals of Machine Learning 

LP 8 
Dauer one semester 
Angebotsturnus in (irregular) alternation with *Machine Learning* 
Format Lecture 4 SWS + Exercise course 2 SWS 
Arbeitsaufwand 240h, thereof 60h lecture 90h tutorials, homework, lecture wrapup 90h graded final report 
Verwendbarkeit cannot be combined with *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 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 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  The lecture, along with its sibling *Advanced Machine Learning*, offers an extended version of the onesemester 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 nonlinear least squares, regularized and sparse regression, robust regression); unsupervised learning (hierarchical clustering, kmeans algorithm, Gaussian mixture models and expectation maximization, principal component analysis, nonlinear dimension reduction); evaluation (risk minimization, model selection, crossvalidation) 

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 