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Lecturer(s)
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Fesl Jan, Ing. Ph.D.
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Konopa Michal, Mgr.
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Course content
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1) Machine Learning, introduction, learning algorithms, metrics evalutation 2) Logical conjuctions generating 3) Reproduction rules generating 4) Decision threes 5) Decision lists and their generating 6) Threshold terms generating 7) Induction of ethalons 8) Lazy learning 9) Reinforcement learning 10) Grupping 11) Combined classifiers, boosting method 12) Combined classifiers, bagging method
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing)
- Preparation for classes
- 60 hours per semester
- Preparation for exam
- 40 hours per semester
- Semestral paper
- 40 hours per semester
- Class attendance
- 30 hours per semester
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Learning outcomes
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The main goal of this coursce is the theoretical and practical introduction into the problematics of the Machine Learning.
An overview of modern machine learning algorithms and the ability to apply them to practical problems.
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Prerequisites
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Basic knowledge of computational and artificial intelligence.
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Assessment methods and criteria
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Oral examination, Test
Writing a semester test with a success rate of over 50% and elaboration of the semestral project.
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Recommended literature
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Machová K, Strojové učenie v systémoch spracovania informací, 2010.
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Machová K., Strojové učenie, principy a algoritmy, 2002.
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Marshall, S., Machine Learning: An Algorithmic Perspective, Second Edition, 2014.
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Mitchell, T.M., Machine Learning. The McGraw-Hill Companies, Inc. New York, 1997.
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