| Detailinformationen |
| Quellcurriculum |
Masterstudium Computer Science 2021S |
| Ziele |
(*)Students will learn, in a practical, exploratory way, what it means to apply standard machine learning algorithms to real-world problems, by experimenting with learning and classification algorithms in a non-trivial application task. This will give them a critical understanding of the complexity of real-world learning, the methodology of systematic experimentation, critical evaluation of results, and possible pitfalls.
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| Lehrinhalte |
(*)Using public machine learning toolboxes, students will go through all the stages of a pattern classification project of real-world complexity, from annotation, feature definition and extraction to the training of various classifiers and systematic experimentation.
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| Beurteilungskriterien |
(*)Several written project reports throughout the course of the project.
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| Lehrmethoden |
(*)Independen work by students / student groups. Joint discussion of practical work. Presentations by lecturer and selected students or student groups.
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| Abhaltungssprache |
Englisch |
| Literatur |
(*)Will be provided if needed.
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| Lehrinhalte wechselnd? |
Nein |
| Äquivalenzen |
in collaboration with 921PECOMLPV20: VL Machine Learning and Pattern Classification (3 ECTS) equivalent to
921PECOMLPK13: KV Machine Learning and Pattern Classification (4.5 ECTS)
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