[ 921PECOMLPK13 ] KV Machine Learning and Pattern Classification
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| Es ist eine neuere Version 2019W dieser LV im Curriculum Master's programme Bioinformatics (discontinued) 2019W vorhanden. |
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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 Computer Science 2016W |
| Objectives |
To provide an overview of standard methods in the fields of pattern classification, machine learning, and statistical data modelling. To explain the basic concepts and methods in the field, and demonstrate the applicability of these methods to a variety of complex problems, and to permit student to experiment with learning and classification algorithms in a complex, non-trivial real-world application task.
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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 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 |
Written exam at the end of the semester; practical project to be carried out (in groups) during the semester.
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| Methods |
Slide presentation with case studies on the blackboard
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| Language |
English |
| Study material |
Will be announced in the first lecture
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| Changing subject? |
No |
| Corresponding lecture |
(*)INMPPKVMLPC: KV Machine Learning and Pattern Classification (4,5 ECTS)
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| On-site course |
| Maximum number of participants |
- |
| Assignment procedure |
Direct assignment |
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