| Fundamentals of Machine Learning [2024 SoSe] | ||
|---|---|---|
| Code IFML |
Name Fundamentals of Machine Learning |
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| CP 8 |
Duration one semester |
Offered in (irregular) alternation with *Machine Learning* |
| Format Lecture 4 SWS + Exercise course 2 SWS |
Workload 240h, thereof 60h lecture 90h tutorials, homework, lecture wrap-up 90h graded final report |
Availability cannot be combined with *Machine Learning* M.Sc. Angewandte Informatik M.Sc. Data and Computer Science M.Sc. Scientific Computing |
| Language English |
Lecturer(s) Ullrich Köthe |
Examination scheme |
| Learning objectives | 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. |
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| Learning content | 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) |
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| Requirements for participation | recommended are: solid knowledge of basic calculus, statistics, and linear algebra | |
| Requirements for the assignment of credits and final grade | The module is completed with a graded written examination. This examination is a report on a 90 h mini-research project. The final grade of the module is determined by the grade of the examination. The requirements for the assignment of credits follows the regulations in section modalities for examinations. Details will be given by the lecturer. | |
| Useful literature | Trevor Hastie, Robert Tibshirani, Jerome Friedman: The Elements of Statistical Learning (2nd edition), Springer, 2009 | |