Machine Learning
(in German: Machine Learning - )
Module-ID: FIN-INF-102825 |
| Link: | LSF |
| Responsibility: | Andreas Nürnberger |
| Lecturer: | Andreas Nürnberger |
| Classes: |
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| Applicability in curriculum: | - M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Fundamentals of Data Science - M.Sc. DE: Grundlagen Informatik - M.Sc. VC: Computer Science |
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Abbreviation ML |
Credit Points 6 |
Semester Winter |
Term ab 1. |
Duration 1 Semester |
Language english |
Level Master |
Intended learning outcomes:
Students ...
- can create machine learning pipelines and individual algorithms
- program decision trees, multi-layer perceptrons, and KNN classifiers in Java, Python, C++
- can evaluate the predictive performance of various classifiers for practical scenarios
Content:
- Introduction to concept spaces and concept learning
- Algorithms for instance-based learning and cluster analysis
- Algorithms for generating decision trees
- Bayesian learning
- Neural networks
- Association learning
- Reinforcement learning
- Hypothesis evaluation
Workload:
56 contact hours + 124h self study
| Type of examination: | Teaching method / lecture hours per week (SWS): |
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Written exam (also for pass/fail grade). Requirements for exam participation will be announced in the first week of the course in class and online. |
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| Prerequisites according to examination regulations: | Recommended prerequisites: |
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keine |
Algorithmen und Datenstrukturen |
| Media: | Literature: |
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Comments:
Can only be credited if the module "Grundlagen des Maschinellen Lernens" has not been credited already.