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Machine Learning I: Algorithms and Applications
Introduction to Machine Learning
Last Updated: 2026-02-05 15:00:07
Abstract
The course introduces fundamental concepts and algorithms of machine Learning:Bayes decision theory and maximum likelihood estimation,cross-validation, jackknife and bootstrap, hypothesis testing,classification techniques: perceptron, support vector machines,density estimation, unsupervised learning, hidden markov models,dimensionality reduction techniques
Objective
Fundamentals of Machine Learning for data analysis and model building
Content
Machine learning algorithms are data analysis methods which search data sets for patterns and characteristic structures. Typical tasks are the classification of data, automatic regression and unsupervised model fitting. Machine learning has emerged mainly from computer science and artificial intelligence, and draws on methods from a variety of related subjects including statistics, applied mathematics and more specialized fields, such as pattern recognition and neural computation. Applications are, for example, image and speech analysis, medical imaging, bionformatics and exploratory data analysis in natural science and engineering. This course is intended as an introduction to machine learning. It will review the necessary statistical preliminaries and provide an overview of commonly used machine learning methods. Planned topics include: - Bayes Decision Theory and Maximum Likelihood Estimation - Cross-validation, Jackknife and Bootstrap, Hypothesis Testing - Classification Techniques: Perceptron, Support Vector Machines, and others - Density Estimation - Unsupervised Learning - Hidden Markov Models - Reinforcement Learning - Dimensionality Reduction Techniques Further and more advanced topics will be discussed in the follow-up course Machine Learning II (summer semester 2005) by Prof. J. M. Buhmann.
Resources
Lecture Notes
nicht vorhanden; die Vorlesungsfolien werden zur Verfügung gestellt.
Literature
1) R. Duda, P. Hart, D. Stork, Pattern: Classification (2001) John Wiley & Sons 2) T. Hastie, R. Tibshirani, J. Friedman: The Elements of Statistical Learning (2001) Springer
General Information
- Language
- English
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- oral 15 minutes
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | Introduction to Machine Learning |
|
2 h weekly |
| exercise | Introduction to Machine Learning |
|
1 h weekly |