| Detailed information |
| Original study plan |
Master's programme Computer Science 2020W |
| 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, Naive Bayes, feedforward neural networks, support vector machines, ensemble methods); 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; joint discussion of practical work
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| Language |
English |
| Study material |
Will be announced in the first lecture
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| Changing subject? |
No |
| Corresponding lecture |
(*)in collaboration with 921PECOMLPU20: UE Machine Learning and Pattern Classification (1.5 ECTS) equivalent to
921PECOMLPK13: KV Machine Learning and Pattern Classification (4.5 ECTS)
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