Artificial Intelligence - Lecture Material, Simple Python Code Examples and Exercises

This repository contains lecture material, simple Python code examples, and programming exercises 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.

Prerequisites

Studying the material requires:

• Python programming skills
• Practical knowledge of how to implement data structures (Big-O notation, search trees)
• Knowledge of probability theory and combinatorics basics

Here is HOWTO Cover these topics if you feel week on one of these topics.

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

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

© 2025 Michael Hahsler. All code and documents in this repository are provided under Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) License.