Introduction to AI (NAIL120)

An introductory course covering basic concepts and methods of artificial intelligence. The course assumes knowledge of logic and probability theory at the undergraduate level.

Spring 2021/22

The group sessions are remote using Zoom. The language is English if at least one participants does not understands Czech. If you want to attend these sessions, and you did not receive the Zoom link, please send me an email. All students wishing to attend these sessions should join my group in ReCodEx.

Credit ("Zápočet")

A credit is given for obtaining at least 7 * number_of_assignments points. I.e., in this semester it is required to achieve at least 63 points from homeworks. If someone remains just below this threshold, their participation on weekly practicals will be considered.

Assignments

Please download and regularly update the git repository with weekly assignments. You are typically given a partial implementation of some problem in Python, and your task is to complete it. Testing is done automatically via ReCodEx. For passing the tests it is necessary that the interface remains unchanged. Some assignments require installation of additional libraries, which is always clarified in the description.

Slides

  • February 15: General info, requirements, examples (pdf)
  • February 22: Graph vs tree search, BFS, DFS, best first search, uniform cost search, A*, heuristics (pdf)
  • March 1: CSP: consistency techniques, examples of CSP models (pdf)
  • March 8: Logical reasoning: reminder of several basic terms in logic, CNF, resolution, SAT modeling.
    Solution to assignment #1. (pdf)
  • March 15: Automated planning: classical planning, SATPlan, PDDL, examples (pdf)
  • March 22: Probability theory: dependent vs independent events, conditional probability, Bayes theorem, conditional independence (pdf)
  • March 29: Probabilistic reasoning: applications of Bayes theorem (naive Bayes classifier), Bayes networks (pdf)
  • April 7: Probabilistic reasoning over time: basic inference tasks, Markov chain, Hidden Markov model, Viterbi algorithm (pdf)
  • April 19: Decision making: Markov decision processes, Bellman equation, value and policy iteration (pdf)
  • April 26: Games and multi-agent systems: single move games (date night dilemma), minimax, alpha-beta, auctions (pdf)
  • May 10: Machine learning: supervised vs. unsupervised learning, linear regression, classification, decision trees, clustering (pdf)