Artificial Intelligence

Boston University - Spring 2023

Class is held in HAR 324 on Monday and Wednesday 2:30-3:45pm

Course staff and office hours

Instructor: Prof. Iddo Drori, Wednesday, 4:15-5:15pm, CCDS 839

Teaching fellow: Isidora Chara Tourni, Wednesday 11:30am-12:30pm, CCDS 821

Grader: Jeya Varshini Bharath

Grader: Jennifer Jordahl

Textbooks

Artificial Intelligence: A Modern Approach, 4th Ed., Stuart Russell and Peter Norvig, Pearson, 2021

The Science of Deep Learning, Iddo Drori, Cambridge University Press, 2022

Enrolled students receive a free online version

First Day of Classes (Thursday, January 19)

Part I

Lecture 1 (Monday, January 23): Introduction

Lecture 2 (Wednesday, January 25): Neural networks

Lecture 3 (Monday, January 30): Neural networks

Lecture 4 (Wednesday, February 1): Transformers

Lecture 5 (Monday, February 6): Transformers

Part II

Lecture 6 (Wednesday, February 8): Markov decision processes

Lecture 7 (Monday, February 13): Reinforcement learning

Lecture 8 (Wednesday, February 15): Reinforcement learning

Presidents’ Day Holiday (Monday, February 20) No classes

Lecture 9 (Tuesday, February 21): Deep reinforcement learning

Lecture 10 (Wednesday, February 22): Games

Lecture 11 (Monday, February 27): Games

Part III

Lecture 12 (Wednesday, March 1): Search

Spring Recess (Saturday, March 4 - Sunday, March 12)

Lecture 13 (Monday, March 13): Automated machine learning

Lecture 14 (Wednesday, March 15): GPT-4

Part IV

Lecture 15 (Monday, March 20): Rule-based systems

Lecture 16 (Wednesday, March 22): Constraint satisfaction

Lecture 17 (Monday, March 27): Decision trees

Lecture 18 (Wednesday, March 29): Bayesian networks

Lecture 19 (Monday, April 3): Meta learning

Lecture 20 (Wednesday, April 5): Logic

Part V

Lecture 21 (Monday, April 10): Competition results

Lecture 22 (Wednesday, April 12): Artificial general intelligence

Patriots’ Day Holiday (Monday, April 17), No classes

Lecture 23 (Wednesday, April 19): Artificial general intelligence

Part VI

Lecture 24 (Monday, April 24): Applications

Lecture 25 (Wednesday, April 26): Applications

Projects

Lecture 26 (Monday, May 1): Presentations

Lecture 27 (Wednesday, May 3): Presentations

Labs

Lab 1 (Wednesday, January 25): Python

Lab 2 (Wednesday, February 1): Neural networks

Lab 3 (Wednesday, February 8): Transformers

Lab 4 (Wednesday, February 15): Markov decision processes

Lab 5 (Wednesday, February 22): Reinforcement learning

Lab 6 (Wednesday, March 1): Games

Lab 7 (Wednesday, March 15): Search

Lab 8 (Wednesday, March 22): Constraint satisfaction

Lab 9 (Wednesday, March 29): Bayesian networks

Lab 10 (Wednesday, April 5): Rule-based systems

Exercises: quiz and programming homework

Exercise 1: Neural networks

Exercise 2: Transformers

Exercise 3: Convolutional neural networks

Exercise 4: Markov decision processes and RL

Exercise 5: Games and search

Exercise 6: Constraint satisfaction problems

Exercise 7: Bayesian networks

Last Day of Classes (Wednesday, May 3)