glouppe/info8006-introduction-to-ai
Lectures for INFO8006 - Introduction to Artificial Intelligence, ULige
| repo name | glouppe/info8006-introduction-to-ai |
| repo link | https://github.com/glouppe/info8006-introduction-to-ai |
| homepage | |
| language | Python |
| size (curr.) | 387519 kB |
| stars (curr.) | 217 |
| created | 2017-08-31 |
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INFO8006 - Introduction to Artificial Intelligence
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: info8006@montefiore.ulg.ac.be
- When: Fall 2019, Thursday 8:30 AM.
- Classroom: 0.89 Domat / B31
Agenda
| 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] |
Projects
Pacman programming projects
- 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)
Reading assignment
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.
