Teaching




Introduction to Artificial Intelligence (IAI/ZUM)

Artificial Intelligence: a modern approach

Introductory course in artificial intelligence (BI-ZUM). The course covers essential concepts necessary for further study of artificial intelligence. Suitable for undergraduate students.

The course covers the following topics among others:

  • search space, uninformed/informed search, heuristic search
  • planning, satisfiability, constraint satisfaction
  • neural networks, evolutionary computing
  • multi-agent systems, games, data mining


The course is provided in Czech and English language and is accompanied by a seminar. Attandants can chose one of several seminars taught in Czech. One seminar is provided in English.

References:
Peter Norvig, Stuart J. Russell: Artificial intelligence: a modern approach (3rd edition). Prentice Hall/Pearson, 2009.

The course is usually scheduled in summer semester. Lecture slides and additional information can be found on: Course Pages.




Artificial Intelligence Advanced (AIA/UMI)

Artificial Intelligence Books     

Advanced course in artificial intelligence (MI-UMI). The course covers in-depth selected topics in artificial intelligence. Suitable for master students.

The following topics will be covered among others:

  • problem solving, constraint satisfaction and search
  • satisfiability and logic reasoning
  • planning and acting, problem modeling
  • motion planning and robotics

The course is provided in the Czech language and is accompanied by a seminar. Attandants can chose one of several seminars taught in Czech.

References:
Peter Norvig, Stuart J. Russell: Artificial intelligence: a modern approach (3rd edition). Prentice Hall/Pearson, 2009.
Decher, R.: Constraint Processing. Morgan Kaufmann, 2003.
Ghallab, M., Nau, D., Traverso, P.: Automated Planning and Acting. Cambridge University Press, 2016.
Biere, A., Heule, M., Van Maaren, H., Walsh, T.: Handbook of Satisfiability. IOS Press, 2009.
Lažanský, J., Mařík, V., Štěpánková, O., a kolektiv: Umělá inteligence (1) - (6). Academia, 2000 - 2013.

The course is usually scheduled in winter semester. Lecture slides and additional information can be found on: Course Pages.

Overview of presence and activity points of students at seminars is listed on a separate page (password protected).




Neural Networks and Computational Intelligence (NNC/NSV)

Neural Network Books     

Advanced course in neural networks and related topics (PI-NSV). The course focuses on selected topics in neural networks and computational intelligence. There are no regular lectures for this course. Students consult their progess with the supervisor several times per semester instead. The course is suitable for doctoral students.

The following topics are covered by the course:

  • neural network architectures
  • classification and approximation by neural networks
  • advanced gradient methods and evolutionary algorithms
  • machine learning, deep neural networks, deep learning

References:
Haykin, S.: Neural Networks and Learning Machines. 3rd Edition, Prentice Hall, 2009.
Sundararajan, N., Saratchandran, P.: Parallel Architectures for Artificial Neural Networks. IEEE Computer Society Society, 1998.
Šíma, J., Neruda, R.: Theoretical Issues of Neural Networks. MATFYZPRESS, Prague, 1996.
Aggarwal, Charu C.: Neural Networks and Deep Learning. Springer 2018.

Additional information concerning the course can be found on: Course Pages. Students can enroll in the course in summer semester.