Schedule

Date Topic Notes Reading Assignment out/due
1/6 Introduction Course Introduction
  • A brief history of AI
  • AI in today's world
  • Course Details
  • Questionnaire
 Chapter 1  Look at official Python Tutorial.
1/9 Agents and Problem Domains Agents and Their Problems Ch 2
1/13 Uninformed Search Search I
 Ch 3.1 -- 3.4  
1/16 Informed Search Search II
 Ch 3.5 --3.6, 4.1  
1/20 No class (MLK Day)    
1/23 Informed Search (cont.) and Adversarial Search Competition in games Ch 5.1 -- 5.3, 5.5 Problem set 1 due
1/27 Adversarial Search (cont.)

  
1/30 Uncertainty and Probability
Ch 12.1 -- 12.5  Problem set 2 due
2/3 Graphical Models/Bayes Nets Probabilistic modeling Ch 13.1 -- 13.2 Project description out
2/6 Exact Inference
Ch 13.3
Problem set 3 due
2/10 Approximate Inference
Ch 13.4
  
2/13 Exam 1

2/17 No class (President's Day)    
2/20 Markov Models Sequential modeling  Ch 14.1 -- 14.3 Project proposal due
2/24 Markov Decision Processes (MDPs) Incorporating actions
 Ch 17.1 -- 17.2  
2/27 Planning with MDPs   Ch 5.4 (MCTS), (optional: SB 3.1--3.3, 3.5--3.6, SB 4.1--4.4) Problem set 4 due 
3/3 No class (Spring break)
 
3/6 No class (Spring break)    
3/10 Reinforcement Learning  Learning for MDPs
  Ch 22  
3/13 Reinforcement Learning (cont.)   (optional: SB 6.5)
Probem set 5 due
3/17 Intro to Machine Learning Supervised learning
 Ch 19.1 -- 19.2 Programming assignment 1 due
3/20 More Supervised Learning
Ch 19.4 -- 19.7 
3/24 Deep Learning
Ch 21
 Problem set 6 due
3/27 More deep learning and Deep RL


3/31
 Exam 2


4/3
 Advanced topics

Programming assignment 2 due
4/7 Project Presentations


4/10 Project Presentations


4/14 Project Presentations


4/21
 Project Reports Due

Report due at 11:59 PM -- This is a hard deadline, no extensions


Important note: unless noted otherwise, all readings and assignments are due on the day that they appear in the schedule.

Unless noted otherwise, all readings are from Artificial Intelligence: A Modern Approach, 4th Ed., Russell and Norvig.