Schedule

Date Topic Notes Reading Assignment out/due
1/9 Introduction Course Introduction
  • A brief history of AI
  • AI in today's world
  • Course Details
  • Questionnaire
 Chapter 1  Look at official Python Tutorial.
1/11 Agents and Problem Domains Agents and Their Problems Ch 2
1/16 Uninformed Search Search I
 Ch 3.1 -- 3.4  
1/18 Informed Search Search II
 Ch 3.5 --3.6, 4.1
1/23 Informed Search (cont.) Search III   Assignment 1 due
1/25 Adversarial Search Competition in games Ch 5.1 -- 5.3, 5.5  
1/30 Adversarial Search (cont.)

Ch 12.1 -- 12.5 		  
2/1 Uncertainty and Probability
  Project description out
2/6 Graphical Models/Bayes Nets Probabilistic modeling Ch 13.1 -- 13.2 Assignment 2 due
2/8 Exact Inference
Ch 13.3
 
2/13 Approximate Inference
Ch 13.4
  
2/15 Exam 1

2/20 Markov models Sequential modeling Ch 14.1 -- 14.3  
2/22 Markov decision processes (MDPs) Incorporating actions Ch 17.1 -- 17.2 Project proposal due
2/27 Planning with MDPs
Ch 5.4 (MCTS), (optional: SB 3.1--3.3, 3.5--3.6, SB 4.1--4.4) Assignment 3 due
2/29 Reinforcement learning Learning for MDPs Ch 22
3/5S No class (Spring break)
 
3/7 No class (Spring break)    
3/12 Reinforcement learning (cont.) (optional: SB 6.5)
3/14 Intro to machine learning  
 Ch 19.1 -- 19.2  
3/19 More supervised learning 
Ch 19.4 -- 19.7
Assignment 4 due
3/21 Unsupervised learning
 
3/26 Deep learning
Ch 21
3/28 More deep learning


4/2 Deep reinforcement learning


4/4
 Exam 2


4/9
 Advanced topics

Assignment 5 due
4/11 Project Presentations


4/16 Project Presentations


4/18 Project Presentations


4/23
 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.