FI:P034 Machine Learning - Course Information

P034 Machine Learning

Faculty of Informatics
Autumn 1996

Extent and Intensity

2/0. 2 credit(s). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).

Teacher(s)

doc. Ing. Jan Žižka, CSc. (lecturer)

Guaranteed by

Contact Person: doc. Ing. Jan Žižka, CSc.

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

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