Lectures for INFO8006 - Introduction to Artificial Intelligence, ULiège, Fall 2019.
- Instructor: Gilles Louppe
- Teaching assistants: Antoine Wehenkel, Samy Aittahar, Pascal Leroy and Florian Merchie
- Contact: [email protected]
- When: Fall 2019, Thursday 8:30 AM.
- Classroom: 0.89 Domat / B31
| Date | Topic |
|---|---|
| September 19 | Outline [PDF] Lecture 0: Introduction to artificial intelligence [PDF] Lecture 1: Intelligent agents [PDF] Tutorial: Introduction to Python |
| September 26 | Lecture 2: Solving problems by searching [PDF] Exercises 1: Solving problems by searching [PDF] [Solutions] Project 1: Search algorithms |
| October 3 | Q&A and guidance for project 1 |
| October 10 | Lecture 3: Constraint satisfaction problems [PDF] Exercises 2: Constraint satisfaction problems [PDF] [Solutions] |
| October 13 | Project 1 deadline |
| October 17 | Lecture 4: Games and adversarial search [PDF] Exercises 3: Games and adversarial search [PDF] [Solutions] Project 2: Adversarial search |
| October 24 | Lecture 5: Representing uncertain knowledge [PDF] Exercises 4: Reasoning under uncertainty (part 1) [PDF] [Solutions] |
| October 31 | (no class) |
| November 7 | Lecture 6: Inference in Bayesian networks [PDF] Exercises 5: Reasoning under uncertainty (part 2) [PDF] [Solutions] |
| November 10 | Project 2 deadline |
| November 14 | Lecture 7: Reasoning over time (part 1) [PDF] Exercises 6: Reasoning over time (part 1) [PDF] [Solutions] Project 3 announcement |
| November 21 | Lecture 7: Reasoning over time (part 2) [PDF] Lecture 8: Making decisions [PDF] Exercises 7: Reasoning over time (part 2) [PDF] [Solutions] |
| November 28 | Lecture 9: Learning [PDF] Exercises 8: Making decisions (part 1) [PDF] [Solutions] |
| December 5 | Lecture 9: Learning [PDF] Lecture 10: Communication [PDF] Exercises 9: Making decisions (part 2) [PDF] [Solutions] Exercises 10: Learning [PDF] [Solutions] |
| December 8 | Project 3 deadline |
| December 12 | Crash test exam |
| December 19 | Lecture 11: Artificial general intelligence and beyond [PDF] Grading of the crash test exam |
| --- | All lectures [PDF] |
- General instructions
- Part 1: Search algorithms (due by October 13)
- Part 2: Adversarial search (due by November 10)
- Part 3: Bayes filter (due by December 8)
Your task is to read a major scientific paper in the field of Artificial Intelligence.
Paper: "Mastering the game of Go with deep neural networks and tree search"
David Silver et al, 2016. [PDF]
The reading assignment includes the main text (pages 1-6), as well as the methods section (pages 7-9).
Short questions will be asked as part of the written exam. You do not have to produce any summary report.