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Lecturer(s)
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Hájek Petr, prof. Ing. Ph.D.
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Course content
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Machine learning concept and learning theory Machine learning tasks Generative learning algorithms Support vector machines Ensembles of algorithms for supervised learning Algorithms for unsupervised learning Hybrid learning systems Evaluating learning algorithms Reinforcement learning Machine learning applications
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming), Work with text (with textbook, with book), Methods of individual activities, Laboratory work
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Learning outcomes
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The subject aims to develop a general understanding of machine learning approaches. The subject covers the fundamental concepts of machine learning and introduces basic tasks for machine learning. The tasks are further divided into supervised, unsupervised and reinforcement learning.
Students will understand the fundamental methods of machine learning. Students will learn to apply them and develop machine learning systems for specific tasks.
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Prerequisites
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Basic skills in PC and MS Excel utilization.
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Assessment methods and criteria
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Written examination, Home assignment evaluation, Systematic monitoring
Assignment: successful elaboration of given tasks (minimum 60%) and successful defend of two practical projects. Examination: based on projects' assessment (40%) and written examination (60%), the written examination with minimum 60% marks.
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Recommended literature
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ALPAYDIN, E. Introduction to Machine Learning. London, 2009.
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BISHOP, C.M. Pattern Recognition and Machine Learning. New York, 2007.
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DUDA, R., HART, P., STORK, D. Pattern Classification. New York, 2001.
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HASTIE, T., TIBSHIRANI, R., FRIEDMAN, J. The Elements of Statistical Learning. Berlin, 2009.
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MITCHELL, T. Machine Learning. New York, 1997.
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WITTEN, I.H., FRANK, E., HALL, M.A. Data Mining: Practical Machine Learning Tools and Techniques. Amsterdam, 2011.
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