[IFML] - [de] - [Fundamentals of Machine Learning]


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 wrap-up
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 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 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)
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