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Introduction to AI (2023/2024)
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. Each assignment is worth at least 10 points. The threshold for passing is $7 \times \text{number of assignments}$. The expected number of assignments is $10$, but this may change due to some unforeseen circumstances.
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
 Total coloring 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.
 20.2., 21.2.

 27.2., 28.2.

 Search algorithms, search space vs. the input graph.
 Graph search vs. tree search.
 A*, admissible and monotonous heuristics.
 4.3., 6.3.

 CSP
 Solving  backtracking, forward check, look ahead, AC3
 Modeling  Sudoku, graph coloring, SEND+MORE=MONEY.
 12.3., 13.3.

 SAT
 Why we want to use CNF, why not to use DNF.
 Solving  DPLL, unit propagation, pure literal elimination
 Modeling  at least one, at most one, sudoku, graph coloring
 19.3., 20.3.

 Planning
 Modeling in PDDL. Transport car with 2 boxes.
 Tower of Hanoi, Npuzzle, Hamiltonian cycle.
 26.3., 27.3.

 Probability reasoning
 Bayesian rule, conditional probability, chain rule.
 Wumpus world and connection to minesweeper.
 Continuing with Bayesian networks next time. The deadline for homework is 3 weeks this time.
 2.4., 3.4.

 Bayesian networks
 Enumeration and variable elimination.
 9.4., 10.4.

 Markov chain (MC) and hidden Markov model (HMM).
 Prediction of sunny/rainy days by MC.
 Filtering and smoothing for HMM. Nice slides with solved examples may be found here.
 16.4., 17.4.

 MDP.
 Value iteration, policy iteration.
 23.4., 24.4.

 Games.
 Turnbased games, minimax, alphabeta. What are disadvantages of minimax and how to solve them.
 Single turn games, game theory, dominant strategy, Nash equilibrium, pure and mixed strategies.
 30.4., 15.5.

 First part of machine learning.
 Regression, kmeans, decision trees (will finish next time).
 7.5., 22.5.

 Second part of machine learning.
 Neural networks, reinforcement learning.
 21.5.

 Bonus lecture.
 Modern techniques. MCTS, Alpha[Go, ZeroGo, Zero], transformers.