[ INMPPKVMLPC ] KV (*)Machine Learning and Pattern Classification
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| (*) Unfortunately this information is not available in english. |
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| Workload |
Education level |
Study areas |
Responsible person |
Hours per week |
Coordinating university |
| 4,5 ECTS |
M1 - Master's programme 1. year |
Computer Science |
Gerhard Widmer |
3 hpw |
Johannes Kepler University Linz |
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| Detailed information |
| Original study plan |
Master's programme Pervasive Computing (discontinuing) 2012W |
| Objectives |
(*)To provide an overview of standard methods in the field of pattern classification, machine learning, and statistical data modelling. To explain the basic concepts and methods in the field, and demonstrate the application of these methods in a variety of complex tasks.
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| Subject |
(*)Bayes classification and Bayes error; density estimation; nearest-neighbour classification; standard classifiers in machine learning (decision trees, rules, Naive Bayes, feedforward neural networks, support vector machines); empirical evaluation of classifiers; clustering and (Gaussian) mixture models; dimensionality reduction and data projection methods; Markov processes and Hidden Markov Models.
Practical Track: Students will carry out a pattern classification project of real-world complexity in several stages, from feature definition and extraction to the training of various classifiers and systematic experimentation.
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| Criteria for evaluation |
(*)Schriftliche Prüfung am Ende des Semesters; Durchführung eines praktischen Projekts (in Gruppen) während des Semesters.
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| Language |
English |
| Changing subject? |
No |
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| On-site course |
| Maximum number of participants |
35 |
| Assignment procedure |
Direct assignment |
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