Introduction to Machine Learning
Course 0000005147 in SS 2021
General Data
| Course Type | lecture with integrated exercises |
|---|---|
| Semester Weekly Hours | 4 SWS |
| Organisational Unit | Assistant Professorship of Machine Learning (Prof. Heckel) |
| Lecturers |
Reinhard Heckel Tobit Klug |
| Dates |
Assignment to Modules
-
EI04016: Introduction to Machine Learning / Introduction to Machine Learning
This module is included in the following catalogs:- Further Modules from Other Disciplines
Further Information
Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.
| additional remarks | The course provides an introduction to concepts, methods, best practices, and theoretical foundations of standard machine learning algorithms. Topics covered include regression, classification, model selection and validation, kernels, nearest neighbor algorithms, clustering, decision trees, ensemble learning, empirical risk minimization and regularization. |
|---|---|
| Links |
E-Learning course (e. g. Moodle) TUMonline entry |
Equivalent Courses (e. g. in other semesters)
| Semester | Title | Lecturers | Dates |
|---|---|---|---|
| SS 2022 | Introduction to Machine Learning | Heckel, R. Klug, T. |
Tue, 09:45–11:15, 0606 Tue, 11:30–13:00, 0606 |
| SS 2020 | Introduction to Machine Learning | Heckel, R. | |
| SS 2019 | Introduction to Machine Learning | Heckel, R. |