Here is a brief diary of topics discussed in classes (see also the class calendar for the AIML-I course in 24/25 here).

Slides and coding scripts are distributed after each lecture via the Google groups mailing-list. Refer to the home page on how to subscribe.

[25/02/2026] Introduction of Artificial Intelligence and Machine Learning

[27/02/2026] Rational agents

[04/03/2026] State space graphs, search trees and uninformed search

[06/03/2026] Depth-First Search, Breadth-First Search, Uniform-Cost Search

[11/03/2026] Informed search: greedy search

[13/03/2026] Informed search: A*

[18/03/2026] Graph search with A* and hill climbing

[20/03/2026] Simulated annealing, beam search, genetic algorithms

[25/03/2026] Constraint Satisfaction Problems and Backtracking Search

[27/03/2026] Improving Backtracking Search by ordering, filtering and leveraging the problem structure

[01/04/2025] Games and Minimax Search

[15/04/2026] Alpha-Beta Pruning

[17/04/2026] Expectimax, state value evaluation functions, Montecarlo Tree Search and Introduction of Markov Decision Processes

[22/04/2026] Markov Decision Processes value iteration 

[24/04/2026] Policy iteration

[29/04/2026] Introduction of (passive) Reinforcement Learning

[6/05/2026] Model-based approaches, direct (Monte Carlo) evaluation and Temporal Difference Learning

[8/05/2026] Q-learning; Exploration methods; Linear value functions; Policy search

[13/05/2026] Propositional logic

[15/05/2026] Inference in Propositional logic

[20/05/2025] First-order Logic

[22/05/2025] Review of exam exercises

[27/05/2025] Exam simulation

[29/05/2025] Q&A Discussion on the exam and course content