Bi0707 Machine learning

Faculty of Science
Spring 2007
Extent and Intensity
0/0. 4 credit(s) (plus extra credits for completion). Type of Completion: z (credit).
Teacher(s)
Prof. Miroslav Kubát, Ph.D. (lecturer), doc. Ing. Jan Žižka, CSc. (deputy)
RNDr. Danka Haruštiaková, Ph.D. (assistant)
Guaranteed by
doc. Ing. Jan Žižka, CSc.
RECETOX – Faculty of Science
Contact Person: doc. Ing. Jan Žižka, CSc.
Prerequisites
programming skills necessary knowledge of artificial intelligence welcome
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The discipline of machine learning investigates algorithms that help computers acquire skills that it would be difficult to impart by traditional programming. The ability to recognize complex objects or the ability to interact with a complex environment are good examples. In this course, the students will learn how to write simple inductive programs, understand their strengths and weaknesses, and develop some idea about how to apply these algorithms in real-world applications.
Syllabus
  • Introduction. Essential tasks for machine learning. Decision trees. Instance-based learning. Artificial neural networks. Computational learning theory. Genetic algorithms. Learning logical descriptions. Reinforcement learning. Conceptual cluster analysis.
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught only once.
The course is taught: in blocks.
Information on the extent and intensity of the course: 5 dní 4 hodiny denně.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/sci/spring2007/Bi0707