Search
	Implement depth-first, breadth-first, uniform cost, and A* search algorithms. 
	Used to solve navigation and traveling salesman problems in the Pacman world.

Multi-Agent Search 
	Classic Pacman is modeled as both an adversarial and a stochastic search problem.
	Implement multiagent minimax and expectimax algorithms, as well as designing evaluation functions.

Reinforcement Learning 
	Implement model-based and model-free reinforcement learning algorithms, 
	applied to the AIMA textbook's Gridworld, Pacman, and a simulated crawling robot.

Ghostbusters 
	Probabilistic inference in a hidden Markov model tracks the movement of hidden ghosts in the Pacman. 
	Implement exact inference using the forward algorithm and approximate inference via particle filters.

Classification 
	Implement standard machine learning classification algorithms 
	using Naive Bayes, Perceptron, and MIRA models to classify digits. 
	Extend this by implementing a behavioral cloning Pacman agent.