VIK004 Machine Learning

Faculty of Arts
Spring 2004
Extent and Intensity
2/0/0. 3 credit(s). Type of Completion: z (credit).
Teacher(s)
doc. Ing. Jan Žižka, CSc. (lecturer)
Guaranteed by
PhDr. Pavla Kánská
Department of Machine Learning and Data Processing – Faculty of Informatics
Contact Person: Helena Bednářová
Timetable
each odd Friday 8:20–9:55 8
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.
Syllabus
  • Machine Learnig (ML) as the integration of Artificial Intelligence (AI) and cognitive sciences. Computational processes that are related to learning. Selection of learning algorithms.
  • Training and testing data. Solution space. Learning and searching. Natural and human learning. Problem representation language. Learning algorithms with numerical and symbolic inputs.
  • Perceptrons, logical neural networks, Boltzmann machine, Kohonnen maps. Genetic algorithms. Comparison with biological systems.
  • Methods of decision-tree induction. Presence of noise, incomplete description of examples. Utilization of knowledge and the possibility of transformation from decision trees to rules.
  • Pattern recognition. Generalisation. The method of a nearest neighbour (k-NN). Instance-based learnig (IBL). Radial basis functions (RBF).
  • Learning in rule-based systems. Inductive and EBL (deductive) learning.
  • Other learning methods. Reinforcement learning.
  • Mathematical aspects of learning. PAC, VC-dimension, Occam's razor.
Language of instruction
Czech
Further Comments
Study Materials
The course is also listed under the following terms Autumn 2001, Spring 2002, Spring 2003.
  • Enrolment Statistics (recent)
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