Artificial Intelligence - Lecture Material, Simple Python Code Examples and Assignments
This repository contains lecture material, simple Python code examples, and assignments for the course CS 5/7320 Artificial Intelligence taught by Michael Hahsler at the Department of Computer Science at SMU.
The code examples cover several chapters of the textbook Artificial Intelligence: A Modern Approach (AIMA) by Russell and Norvig. The code in this repository is intended to be simple to focus more on the basic AI concepts and less on the use of advanced implementation techniques (e.g., object-oriented design and flexibility). More complex code examples accompanying the textbook can be found at the GitHub repository aimacode.
Studying the material requires
- advanced Python programming skills.
- practical knowledge of how to implement data structures (Big-O notation, search trees).
- a working knowledge of probability theory and combinatorics.
Covered AIMA Chapters
| Module | Chapter | Lecture Slides | Code |
|---|---|---|---|
| 1 | 1: Introduction to AI (+ 27 Ethics and Safety) | PDF, PowerPoint | - |
| 2 | 2: Intelligent Agents | PDF, PowerPoint | Code |
| 3 | 3: Solving Problems by Search | PDF, PowerPoint | Code |
| 4 | 4.1-4.2: Search in Complex Environments: Local Search | PDF, PowerPoint | Code |
| 5 | 4.3-4.5: Search in Complex Environments: Search with Uncertainty | PDF, PowerPoint | Code |
| 6 | 5: Adversarial Search and Games | PDF, PowerPoint | Code |
| 7 | 6: Constraint Satisfaction Problem | PDF, PowerPoint | Code |
| 8 | 7-10: Knowledge-Based Agents | PDF, PowerPoint | Code |
| 9 | 11: Automated Planning: Hierarchical Planning and Monitoring | PDF, PowerPoint | - |
| 10 | 12: Quantifying Uncertainty: Bayesian Decision-Making | PDF,PowerPoint | Code |
| 11 | 13: Probabilistic Reasoning: Bayesian Networks | PDF, PowerPoint | Code |
| 12 | 16: Making Simple Decision: Decision Networks | PDF, PowerPoint | - |
| 13 | 19: Learning from Examples: Supervised Machine Learning | PDF, PowerPoint | Code |
| - | 22+17: Reinforcement Learning and MDPs | PDF, PowerPoint | Code |
Ask the AIMA Scholar (GPT) a question about the content of the textbook.
HOWTOs
- HOWTO install and use Python and Jupyter Notebooks
- HOWTO work on assignments
- HOWTOs for AI with Python with code examples
License
All code and documents in this repository are provided under Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) License.
