This site is the central point for the lectures on Artificial Intelligence (INSA 4IR). It will be updated regularly with the slides and other resources.
Lecture Slides
Below are the slides for the lectures, that will be made available as the course progresses.
- Lecture 1: Introduction (AIMA Chap. 1)
- Lecture 2: The Agent Model (AIMA Chap. 2)
- Lecture 3: Solving problems by searching (AIMA Chap. 3)
- Lecture 4: Utility and Decision Theories (AIMA Chap. 15, Making simple decisions)
- Lecture 5: Markov Decision Processes (AIMA Chap. 16, Making complex decisions)
- Lecture 6: Online MDP: 2048 & Expectimax (AIMA Chap. 16 & 5)
- Lecture 7: Multi-agent environments & Adversarial games (AIMA Chap. 17 & 5)
- Lecture 8: Monte Carlo Tree Search (AIMA Chap. 5)
- Conclusion: Some elements on remaining labs and exams
Working at home: The course is based on the book Artificial Intelligence: A Modern Approach (AIMA), by Stuart Russell and Peter Norvig.
Evaluation
The evaluation will be based on:
- a written exam (~50%)
- exam on moodle, with short open and multiple-choice questions
- date: 7th of May (07/05), 8am
- everything seen in the course and the labs is examinable, including exercises done during the course but not present in the slides
- examples of questions can be found in the last set of slides (conclusion)
- no documents allowed
- a project based evaluation (~50%), based on the last two labs of the course (MCTS for checkers)
- deadline: May the fourth (04/05), 23:59
- the project will be done in groups of 2-3 students
- the project will be evaluated based on the performance of the code and on a short report detailing the evaluation conducted
- modalities: push your code and report (README.md) on the shared github repository