CS 687
Advanced Aritificial Intelligence

Time/Location: Monday 7:20-10pm, Innovation Hall 204
Instructor: Jana Kosecka
Office hours: Wednesday 2-3pm
Contact: Office 4444 Research II, e-mail: kosecka@gmu.edu, 3-1876
Course web page: http://www.cs.gmu.edu/~kosecka/cs687/

Required Textbook:
[1] Russel and Norvig: Artificial Intelligence: A Modern Approach, 3rd edition
[2] Sutton and Barto: Reinforcement Learning: An Introduction
Some materials in this course are adopted from CS188 AI Course


Schedule (subject to change)

Mar 7th)
Date Topic, Handouts Assignments/Due dates Resources
Jan 24 Introduction and course logistics slides.pdf
Machine Learning slides.pdf
Linear Algebra Review slides.pdf
Getting started Project 0 (due Friday Feb 1st)
Chapter 18.6.3
Jan 31 Machine Learning, Neural Networks slides.pdf   Project 1 (due Thursday Feb 21st)
Chapter 18.6-18.8
logistic regression notes
Feb 7 Machine Learning, SVMs, Ensebmle Methods slides.pdf
Instance Based Methods, Approx. NN methods slides.pdf
svm guide
Feb 14 Probabilistic Reasoning, Bayesian Networks slides.pdf
Feb 21 Naive Bayes slides.pdf
Bayes Nets Independence slides.pdf
  Homework 1 (due Feb. 28)
Feb 28 Bayes Nets Inference, Enumeration, Variable Elimination, Sampling slides.pdf
March 7 Temporal Models, HMM's, Kalman Filters, Particle filters slides.pdf
March 14 Spring Break
March 21 Temporal Models Cont, Kalman Filters, Particle filters
  Homework 3 (due Mar 28th)
March 28 Markov Decision Processes slides.pdf
April 4 Reinforcement Learning slides.pdf
April 11 Exam
April 19 Advanced Topics slides.pdf
April 11 Special Topics: Deep Learning, Natural Language Processing, Robotics, Vision
  Project 2 (due May 2nd)