====== Suggested study literature ======
  - [AIMA3] Russel S. a Norvig P.: Artificial Intelligence: A Modern Approach (3rd edition), Prentice Hall, 2010
    * This is the primary textbook for the course. 
  - [10LECT] Schlesinger, M.I. and Hlaváč, V.: Ten Lectures on Statistical and Structural Pattern Recognition. Springer, 2002
    * Used as the source for lecture on Bayesian and Non-Bayesian decision making. Also contains chapters on linear discrimination functions, and hidden Markov models.
    * Česká verze vyšla pod názvem "Deset přednášek ze statistického a strukturálního rozpoznávání" ve vydavatelství ČVUT.
  - [DUDA] Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification (2nd edition). Wiley, 2000
    * Very good alternative text book. 

====== Videolectures ======
The topics of this course can be also learned from other online sources. Here we list some MOOCs with a strong connection to this course.

[[https://www.coursera.org/instructor/andrewng|Andrew Ng]]'s [[https://www.coursera.org/course/ml|Machine Learning]] (at Coursera.org)
  * Suitable as a complement for the first part of our course, lectures 2 to 5.

Pieter Abbeel's and Dan Klein's [[https://www.edx.org/course/artificial-intelligence-uc-berkeleyx-cs188-1x-0#.VOslTy4mfm4|Artificial Intelligence]] (at edX.org)
  * Covers almost all topics of our course and adds some more.
  * Whole set of lecture videos from 2014 can be found at [[https://www.youtube.com/playlist?list=PLNozK-HB4MXsVAN6cqkCAO09RChbIAk5i|youtube]].