| Machine Learning Essentials [2024 SoSe] | ||
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| Code IMLE |
Name Machine Learning Essentials |
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| CP 8 |
Duration one semester |
Offered in (irregular) alternation with *Fundamentals of Machine Learning* + *Advanced Machine Learning* |
| Format Lecture 4 SWS + Exercise course 2 SWS |
Workload Arbeitsaufwand: 240h, thereof 60h lecture 90h tutorials, homework, lecture wrap-up 90h graded final report |
Availability This is the retitled *Machine Learning* module! cannot be combined with *Fundamentals of Machine Learning* or *Advanced 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 a broad range of machine learning concepts, get to know established and advanced 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 | This lecture is a compact version of the two-semester course *Fundamentals of Machine Learning* + *Advanced Machine Learning*: Classification (linear and quadratic discriminant analysis, neural networks, linear and kernelized support vector machines, decision trees and random forests), least squares and regularized regression, Gaussian processes, unsupervised learning (density estimation, cluster analysis, Gaussian mixture models and expectation maximization, principal component analysis, bilinear decompositions), directed probabilistic graphical models, optimization for machine learning, structured learning |
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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 | This is the retitled *Machine Learning* module! 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. |
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| Useful literature | Trevor Hastie, Robert Tibshirani, Jerome Friedman: The Elements of Statistical Learning (2nd edition), Springer, 2009; David Barber: Bayesian Reasoning and Machine Learning, Cambridge University Press, 2012 |
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