Assignments and examples for the course in CS 5/7320 Artificial Intelligence taught at the Computer Science Department at SMU by Michael Hahsler.
The code follows the textbook Artificial Intelligence: A Modern Approach by Russel and Norvig. The code in this repository is intended to be simpler to focus on the basic AI concepts and less on implementation details (e.g., object-oriented design). Other code accompanying the textbook can be found at the GitHub repository aimacode.
- Chapter 2: Intelligent Agents
- Chapter 3: Uninformed and Informed Search
- Chapter 4: Local Search
- Chapter 6: Constraint Satisfaction Problem
more to come.
To install Python on your system, you can
- install Docker and the jupyter/datascience-notebook image (run
docker run -p 8888:8888 jupyter/datascience-notebook, usedocker stop <container id>anddocker start <docker id>to stop and start the container), or - install Python, Jupyter and the needed packages (e.g., via Anaconda), or
- use a service like the Google Colab environment (there are some limitations and you will have to copy some needed files to Google Drive).
The easiest way is to use the Docker image which already contains almost everything we need.
If you are not familiar with Python then you should work through a tutorial like this that covers Python basics and the packages numpy and pandas. Code examples that help with the assignments are available here.
You can fork this repository to work on your solutions locally. Submit a pdf of the compiled notebook (either export the notebook as pdf or print to pdf). The notebook needs to be a complete project report with documentation (including your design choices), code and results (e.g., tables with simulation results) with a short discussion of what they mean. Use the provided notebook cells and insert additional code and markdown cells as needed.
All code and documents in this repository is provided under Creatice Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) License
