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Machine learning is reaching notable success when solving complex tasks in many fields. This course serves as in
introduction to basic machine learning concepts and techniques, focusing both on the theoretical foundation, and
on implementation and utilization of machine learning algorithms in Python programming language. High
attention is paid to the ability of application of the machine learning techniques on practical tasks, in which the
students try to devise a solution with highest performance.
Last update: Vidová Hladká Barbora, doc. Mgr., Ph.D. (15.05.2019)
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After this course, students should…
Last update: Libovický Jindřich, Mgr., Ph.D. (12.03.2024)
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Students pass the practicals by submitting sufficient number of assignments. The assignments are announced regularly through the whole semester (usually two per lecture) and are due in several weeks. Considering the rules for completing the practicals, it is not possible to retry passing it. Passing the practicals is not a requirement for going to the exam. Last update: Straka Milan, RNDr., Ph.D. (10.05.2020)
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Last update: Straka Milan, RNDr., Ph.D. (10.05.2020)
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The exam is written and consists of questions randomly chosen from a publicly known list. The requirements of the exam correspond to the course syllabus, in the level of detail which was presented on the lectures. Last update: Straka Milan, RNDr., Ph.D. (15.06.2020)
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Basic machine learning concepts
Linear regression
Classification
Text representation
Decision trees
Clustering
Dimensionality reduction
Training
Statistical testing
Used Python libraries
This course is also part of the inter-university programme prg.ai Minor. It pools the best of AI education in Prague to provide students with a deeper and broader insight into the field of artificial intelligence. More information is available at prg.ai/minor. Last update: Libovický Jindřich, Mgr., Ph.D. (12.03.2024)
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Basic programming skills in Python and basic knowledge of differential calculus and linear algebra (working with vectors and matrices) are required; knowledge of probability and statistics is recommended. Last update: Straka Milan, RNDr., Ph.D. (08.10.2021)
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