Introduction to AI (2024/2025)

Tutorial for Introduction to Artificial Intelligence

This page is intended for both of the English tutorials.

Textbooks

The lecture is based on Russell and Norvig, Artificial Intelligence: A Modern Approach, 4th Edition (Prentice Hall, 2020)

Organization

To earn the credit, it is necessary to earn enough points for solving programming oriented assignments. There will be 10 assignments, each worth at least 10 points. The threshold for passing is $70$ points.

All of the assignments will be posted on ReCodEx. The source codes to solve the assignments are hosted on Git. All of the assignments are implemented in Python and due to the automatic checks performed by ReCodEx it is necessary to adhere to the provided templates. The deadline will always be around 2 weeks, but for the exact dates, see ReCodEx.

Topics of assignments:

Heuristics for A*
Total coloring using CSP
Partitioning using SAT
Transporting boxes using PDDL
Minesweeper using probability
Localization using MDP
Navigation using Bellman equations
Simple game using Minimax
Predicting diabetes using decision trees (report only)
Article classification using NN (report only)

It is NOT mandatory to attend the classes, however, it is highly recommended as the topics discussed during the class may help you with solving the assignments.

It is forbidden to share the code of your solutions with your colleagues. On the other hand, it is allowed to discuss the approach to solving the assignments.

If someone feels that they are missing the opportunity to earn the credit, do not be afraid to contact me. Such cases will be dealt with individually based on your approach to the exercise.

Lectures

Here will be posted a brief summary of what happened in each lesson. If applicable, a PDF with exercises calculated during the lesson will also be posted.

Monday	Thursday
17.2. Intro, organization info Discussion about current AI trends	20.2. Intro, organization info Discussion about current AI trends
24.2. Cancelled - conference	27.2. Search algorithms, search space, basic distance heuristics. Getting two robots out of a maze.
3.3. Search algorithms, search space, basic distance heuristics. Getting two robots out of a maze.	6.3. Creating heuristics for search problems. Sliding tile puzzle, Sokoban, Our 2 robots, TSP.
10.3. Creating heuristics for search problems. Sliding tile puzzle, Sokoban, Our 2 robots, TSP.	13.3. Solving CSP - backtracking, arc consistency, forward check vs look ahead Modeling in CPS - n-queens, sudoku, graph coloring
17.3. Solving CSP - backtracking, arc consistency, forward check vs look ahead Modeling in CPS - n-queens, sudoku, graph coloring	20.3. Solving SAT - DPLL, unit clause, pure literal Modeling in SAT - sudoku, graph coloring, triangle partitioning
24.3. Solving SAT - DPLL, unit clause, pure literal Modeling in SAT - sudoku, graph coloring, triangle partitioning	27.3. PDDL planning, action schemes/actions, goal state/goal condition Modelling in PDDL - gripper
31.3. PDDL planning, action schemes/actions, goal state/goal condition Modelling in PDDL - gripper	3.4. Probability - Bayes rule, start with Bayesian network Counting probability in Wumpus world
7.4. Probability - Bayes rule, start with Bayesian network Counting probability in Wumpus world	10.4. Finish Bayesian network - variable elimination. Markov chain (MC) and hidden Markov model (HMM). Prediction of sunny/rainy days by MC. Filtering for HMM. Nice slides with solved examples may be found here.
14.4. Finish Bayesian network - variable elimination. Markov chain (MC) and hidden Markov model (HMM). Prediction of sunny/rainy days by MC. Filtering for HMM. Nice slides with solved examples may be found here.	17.4. Finish smoothing MDP - value iteration, policy iteration
21.4. Cancelled - Easter	24.4.
28.4.	1.5. Cancelled - Labor Day
5.5.	8.5. Cancelled - Victory Day
12.5.	15.5.
19.5.	22.5.