Subjects

Your browser does not support JavaScript, or its support is disabled. Some features may not be available.

Artificial Intelligence II - NAIL070

Title:	Umělá inteligence II
Guaranteed by:	Department of Theoretical Computer Science and Mathematical Logic (32-KTIML)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2014 to 2019
Semester:	summer
E-Credits:	3
Hours per week, examination:	summer s.:2/0, Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	taught
Language:	Czech, English
Teaching methods:	full-time
Teaching methods:	full-time
Additional information:	http://ktiml.mff.cuni.cz/~bartak/ui2/

Guarantor:	prof. RNDr. Roman Barták, Ph.D.
Class:	Informatika Mgr. - Teoretická informatika
Classification:	Informatics > Theoretical Computer Science

Opinion survey results Examination dates Schedule Noticeboard

Annotation -

Last update: prof. RNDr. Roman Barták, Ph.D. (05.06.2017)

The course covers uncertainty in artificial intelligence, decision making, and basic methods of machine learning.

Aim of the course - Czech

Last update: prof. RNDr. Roman Barták, Ph.D. (06.10.2017)

Naučit vybrané obecně aplikovatelné techniky umělé inteligence: zpracování neurčitosti, strojové učení, zpracování obrazu, řeči, jazyka

Literature - Czech

Last update: prof. RNDr. Roman Barták, Ph.D. (06.10.2017)

S. Russell, P. Norvig: Artificial Intelligence; A Modern Approach, 2003

F.V. Jensen: Bayesian Networks and Decision Graphs

T. Mitchell: Machine Learning

V. Mařík, O. Štepánková, J. Lažanský a kol.: Umělá Inteligence (1). Academia, Praha

V. Mařík, O. Štepánková, J. Lažanský a kol.: Umělá Inteligence (2). Academia, Praha

Syllabus -

Last update: prof. RNDr. Roman Barták, Ph.D. (05.06.2017)

Uncertainty reasoning: probabilistic methods, Bayesian networks, Markov models.

Decision making: utility theory, Markov Decision Processes, decisions with multiple agents, (inverse) game theory.

Machine learning: supervised learning, decision trees, regression, SVM, boosting; version space search; learning probabilistic models, the EM algorithm; reinforcement learning.