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Suggested study literature

  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.
courses/ui/literature.txt · Last modified: 2019/01/30 09:02 (external edit)