Lesson Plan

What follows is an approximate timeline for the course. We may deviate from this schedule based on the speed of our progress through material, as well as unforseen disruptions (e.g. snowdays, etc.). Related readings for each week are given in italics, and homework assignments are listed in bold.

Week 1: Introduction

We will begin with a discussion of what Artificial Intelligence is, and what disciplines it encompasses. The scope, goals, and policies of the course will also be introduced. We will move on to material listed under Week 2 as time permits.

Russell & Norvig, Chapters 1, 2, & 3

Week 2: Basic Search

State-space search, including both problem mapping and algorithms. Breadth first search, depth first search, A* search.

Homework 1 assigned

Russell & Norvig, Chapter 3

Week 3: Playing Games

We will discuss the problem of state-space search in a multi-agent system such as a two-player game. We will also cover introductory game theory, and how it can be used to solve the optimal play problem.

Russell & Norvig, Chapter 5

Week 4: Logic

Introduction to logic-based problem solving. We will begin with simple Boolean logic, and then cover first order logic.

Homework 1 due, Homework 2 assigned

Russell & Norvig, Chapters 7 & 8

Week 5: Unification and Resolution

We will continue our discussion of logic with the unification and resolution algorithms for performing logical inference. We will also discuss other logic-systems, including higher-order logics and fuzzy logic.

Russell & Norvig, Chapter 9

Week 6: Local Search

We introduce local search algorithms, and discuss their advantages and drawbacks. We will cover classic local search techniques, as well as some evolutionary computing techniques, focusing on the Genetic Algorithm for search.

Homework 2 due, Homework 3 assigned

Russell & Norvig, Chapter 4

Week 7: Concept Learning

We introduce the field of machine learning with the basic concept-learning, or classification, problem. We will discuss the basics of supervised learning and decision trees.

Homework 3 due, Homework 4 & Paper 1 assigned

Russell & Norvig, Chapter 18

Week 8: SVMs & ANNs

We will discuss Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs), and their application to the concept learning problem.

Russell & Norvig, Chapter 18

Spring Break

Week 9: Unsupervised Learning

We will discuss unsupervised and minimally supervised learning, as well as starting to discuss Reinforcement Learning.

Homework 4 due

Russell & Norvig, Chapters 18, 21

Week 10: Reinforcement Learning

We will cover the topic of Reinforcement learning, including both model-based and model-free approaches.

Paper 1 due, Homework 5 & Paper 2 assigned

Russell & Norvig, Chapter 21

Week 11: Probabilistic Models

We will introduce the concepts of reasoning under uncertainty using probabilistic models, including Bayesian Networks. We will also have a discussion about writing scientific papers, in relation to the written portion of the midterm assignment.

Russell & Norvig, Chapters 13, 14 & 20

Week 12: Advanced topics

We will discuss the current state of the art, as well the philosophy of AI.

Homework 5 due

Russell & Norvig, Chapters 26 & 27

Week 13: TBD

Paper 2 due

To be determined based on our progress through the preceding material.