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Modul
Statistical Learning [M-MATH-105840]
Credits
8Recurrence
Jedes SommersemesterDuration
1 SemesterLanguage
Level
4Version
1Responsible
Organisation
- KIT-Fakultät für Mathematik
Part of
Bricks
Identifier | Name | LP |
---|---|---|
T-MATH-111726 | Statistical Learning | 8 |
Competence Certificate
The module will be completed with an oral exam (approx. 30 min).
Competence Goal
At the end of the course, students
- know the fundamental principles and problems of machine learning and can relate learning methods to these,
- are able to explain how selected machine learning methods work and can apply these,
- are able to derive and to discuss a statistical analysis of selected learning methods,
- are able to independently develop and apply new learning methods.
Prerequisites
none
Content
The course aims for a rigorous and mathematical analysis of some popular machine learning methods with a focus is on statistical aspects. Topics are:
- Regression
- Empirical risk minimization
- Lasso
- Regression trees and Random forests
- Classification
- Bayes classifier
- model based classifiers (e.g. logistic regression, discriminant analysis)
- model-free classifiers (e.g. k nearest neighbors, support vector machines)
- Neural networks
- training
- approximation properties
- statistical analysis
- Unsupervised learning
- principle component analysis
- clustering
- generative models
Recommendation
The modules "Probability Theory" and "Statistics" (M-MATH-103220) are recommended.
Workload
Total workload: 240 hours
Attendance: 90 hours
- lectures, problem classes, and examination
Self-studies: 150 hours
- follow-up and deepening of the course content,
- work on problem sheets,
- literature study and internet research relating to the course content,
- preparation for the module examination