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Lecturer(s)
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Outrata Jan, doc. Mgr. Ph.D.
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Course content
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The course repeats the basics of data mining and machine learning, basic data preprocessing methods, classification algorithms, association analysis and clustering and introduce selected newer methods and algorithms. The course extends the introductory parts of the master's studies. Types, quality and data preprocessing, methods of reducing data dimensionality, similarity and dissimilarity of objects. Problem classification, decision trees and other methods. Bayesian networks and graph data models. Ensemble methods (bagging, boosting, random forests), performance evaluation. Association analysis, Apriori algorithm and others. Cluster theory and algorithms, cluster quality. Outliers detection methods. Reinforcement learning. Deep learning.
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Learning activities and teaching methods
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Dialogic Lecture (Discussion, Dialog, Brainstorming), Work with Text (with Book, Textbook)
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Learning outcomes
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Students will expand their knowledge of basic methods of machine learning and data mining and learn the newer and more advanced methods.
1. Knowledge Recognize and understand comprehensively principles and methods of machine learning.
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Prerequisites
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unspecified
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Assessment methods and criteria
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Oral exam
Completing the assignments. Passing the exam.
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Recommended literature
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C. Borgelt, M. Steinbrecher, R. Kruse. Graphical Models Representations for Learning, Reasoning and Data Mining.
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Christopher Bishop. Pattern Recognition and Machine Learning.
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Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning.
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K. P. Murphy. Machine Learning: A Probabilistic Perspective.
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Kruse, R., Borgelt, C., Braune, C., Mostaghim, S., Steinbrecher, M. Computational Intelligence.
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Nielsen, Thomas Dyhre, VERNER JENSEN, FINN. Bayesian Networks and Decision Graphs.
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Pang-Ning Tan Michael Steinbach Vipin Kumar. Introduction to Data Mining.
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T. Hastie R. Tibshirani:. (2016). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.
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