Course Objective

This is an introductory class to artificial intelligence. It’s a really fun class (I think!) since it describes the fundamentals of some important AI systems. In particular, we’ll explore the basics of how AI systems explore and reason about the environments they operate in. So:

• Searching for solutions when there’s no/partial information, and when you are playing against an adversary who wants to beat you.
• Constraint Satisfaction Problems: how can you find a feasible solution under constraints.
• Planning Under Uncertainty: how can an agent model its goals and take actions in order to reach them, in an uncertain (or even adversarial) environment.
• Logical Agents: how can agents model their world using logic, and how can we infer new knowledge based on what we already know.
• Bayesian Inference: just like logic, only better! With uncertainty and relationship between variables, we’ll have fun with probabilities!

This class is going to be focused on theoretical analysis, expect little programming and more theorem proving!

My Responsibility

• Host weekly office hour and be accessible for students in many forms, such as responding piazza in short time
• Prepare and design the programming assignment and exam to help student have a better understanding of the concept
• Grade homework, project, and exam