Artificial Intelligence Programming

The course gives students the opportunity to implement many classic AI algorithms and use them as modules in large AI systems to perform tasks such as speech and image processing, simulated soccer (in the well-known Robocup on-line competition), Texas Hold'Em poker playing, and robot navigation. Some of the important AI methods that can appear in various projects include;

• the A* algorithm
• means-ends analysis
• decision-tree learning
• genetic algorithms
• neural networks
• bayesian classification
• case-based reasoning
• boosting and bagging

Through this work, students will gain an in-depth understanding of "AI in practice" as opposed to the combination of "AI in theory" and "AI on toy problems" that one experiences in the introductory and intermediate AI courses.

The course will consist of 2-4 projects, depending up the year and the extent of the individual projects. Each project will be supported by a series of lectures on relevant theoretical and practical issues surrounding the problem domain, while some class meetings will be reserved for interactive discussions of the problem and student progress.

Students will be free to program in the language of their choice, although Python, Java and C++ will be recommended.

• Students will gain hands-on experience designing and implementing relatively large AI projects.
• Students will gain valuable insights into why, when and how to use AI methods in realistic problems that they may encounter in their technical careers.

50% standard lectures, and 50% interactive project discussions between students and teacher.

Students will be allowed to work alone or in groups of 2 (or possibly larger depending upon the project)