There are two different homework options for the course:
- Do all 3 Individual Classifier Homeworks and the Ensemble
Learning Homework, as described below.
- Learning Game Player
- Build the Learning, Game-Playing system and
- Do ONE of the Individual Classifier Homeworks. However, the classifier that
you implement for part 2 CANNOT be the same learning mechanism
that you use for part 1. So if you use a decision tree
classifier for part 1, then you'll need to implement a Bayesian
or Neural Network for part 2.
The Individual Classifier Homeworks
- Bayesian Network
- Decision Tree
- Neural Network
Combining the Classifiers into an Ensemble Learning System
A Learning, Game-Playing System
- Build an AI system to either play a k-person game (k > 1), such as
Poker, Checkers, Othello, etc., or to solve (1-person) puzzles of a
certain type (e.g. Sudoku, Cryptoarithmetic, etc.).
Demonstrate that the system can solve many puzzles or play many games
(against human and computer opponents). The system should be reasonably
competent (i.e. it should only make legal moves and should not make
too many ridiculous moves), though it need not be superior.
- Add in a machine-learning component to your AI system and
demonstrate that, by using this module, the system can improve its
performance over time. The learning module can be one of those
described in the Classifier Homeworks, or it can be another of your
choosing, such as reinforcement learning or genetic algorithms.
Last modified: Tue Jan 17 11:23:08 MET 2006