Tentative Syllabus


  • Instructor: Amarda Shehu amarda\AT\gmu.edu
    TA: Xiaosheng Li xli22\AT\gmu.edu
    Class Place and Time: Innovation Hall 204, W 4:30-7:10 pm
    TA Office Hours: ENGR #5321, R 3:00-5:00 pm
    Instructor Office Hours: ENGR #4422, W 2:30-4:30 pm
  • Lectures incorporate and adapt material from instructors primarily from UC Berkeley and George Mason University, such as Stuart Russell, Pieter Abeel, Gheorghe Tecucci, and Kenneth A De Jong.

Tentative Syllabus

Date Topic Chapters Assignments
Jan 20 What is AI and Problem Solving? Ch. 1-2 [pdf] , youtube Breakthrough year for AI
Problem-Solving and Search
Jan 27 Problem Solving and Uninformed Search Ch. 2-3 [pdf] , youtube Google beats human at Go
Feb 03 Informed (Heuristic) Search Ch. 3 [pdf], youtube Hw1 Out
Feb 10 Local and Randomized Search/Optimization Ch. 4 [pdf]
Feb 17 Game Playing (Adversarial Search) Ch. 5 [pdf], youtube
Feb 24 Constraint Satisfaction Ch. 6, [pdf], youtube , Freuder 90s paper Hw1 Due, Hw2 Out
Knowledge and Reasoning
Mar 02 Propositional Logic Ch. 7 [pdf]
Mar 09 Spring Break
Mar 16 First-order Logic Ch. 8 [pdf] Hw2 Due
Mar 23 EXAM Ch. 1-7 In class, closed
Mar 30 Inference in First-order Logic Ch. 9 [pdf] good FOL summary
Apr 06 Planning: From Classic to Real-World Ch. 10-11 [pdf]
Uncertainty and Probabilistic Reasoning
Apr 13 Bayesian Networks Ch. 13-14 [pdf], youtube I, youtube II Hw3 Out
Home Reading
Form project team
Apr 20 Inference on Bayesian Networks Ch. 14 [pdf], youtube I , youtube II
Statistical Learning and Other Advanced AI Topics
Apr 27 Temporal Models, Bayesian Learning, Neural Networks Ch. 15, 18, 20 [pdf], youtube I , youtube II , youtube III , , youtube V , youtube VI Hw3 Due
White paper Due (3 pages)
May 06 Final Exam IN 204, 4:30 - 6:30 pm (submit project code and manuscript via blackboard)

Supplementary Step-by-step Video Lectures by Pieter Abeel/Berkeley

DFS and BFS Uninformed Search
A* Search Informed Search
Alpha-Beta Pruning Game Playing
D-Separation Bayes' nets: Syntax and semantics
Elimination of One Variable Bayes' nets: Exact inference
Variable Elimination Bayes' nets: Exact inference
Sampling Bayes' nets: Approximate inference
Perceptron Machine Learning: Neural networks
Maximum Likelihood Machine Learning: Statistical learning
Laplace Smoothing Machine Learning: Statistical learning