1. [AIMA3] Russel S. a Norvig P.: Artificial Intelligence: A Modern Approach (3rd edition), Prentice Hall, 2010
   • This is the primary textbook for the course.
2. [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.
3. [DUDA] Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification (2nd edition). Wiley, 2000
   • Very good alternative text book.

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.

Andrew Ng's 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 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 youtube.