Books, on-line resources, specialization courses

Recommended reading. You can use or own resources which, however, may differ in some terminology.

[Russell-Norvig2010] is the main course book. We will not need the whole book. A more detailed recommendation will be provided throughout the course. Note the large on-line content at http://aima.cs.berkeley.edu. Toto je za

Reinforcement learning (posilované učení) je dobře vysvětleno v [Sutton-Barto98], on-line html. Druhé vydání [Sutton-Barto2017], on-line pdf. Kapitola 4 - Dynamic programming dobře poslouží i pro studium sekvenčního rozhodování (MDPs).

Kniha [LaValle2006] je k dispozici i on-line. Pokrývá mnohem více než budeme potřebovat. Nicméně, kapitoly 2, 9, 10 s hodí i nám. Některé základní koncepty, které budeme diskutovat podrobně, jsou v knize podány kompaktně, ale někomu to může vyhovovat.

For Python we recommend [Wentworth2012] or [Downey2009]; the newer and Python 3.x fully compatible [Wentworth2012] is probably the better option.

You can also use multiple on-line resources:

Artificial Intelligence course at Berkeley. This course has strongly influenced/motivated our course.
Artificial Intelligence course at CTU. A master course, now taught with changed content. SDPs and Reinforcement learning no longer covered there - we do.

CTU courses for possible specializations. If you fall in love with some parts of our course, you may dig deeper in the following courses:

Pattern Recognition and Machine Learning a bachelor course at CTU goes deeper into (statistical) machine learning.
Introduction into Artificial Intelligence a bachelor course at CTU studies deeper games and knowledge representation.
Statistical Machine Learning a master course at CTU goes deep into the theory of statistical machine learning. (Notably: SVM, EM algorithm, Neural Networks, Reinforcement Learning).

You may also consider participating in research projects as a summer intern or doing an individual project or accomplishing a bachelor/master thesis.