CS 687
Advanced Artificial Intelligence

Time/Location: Wednesday 7:20-10pm,   Art and Design Building L008
Instructor: Jana Kosecka
Office hours:  2-3pm Wednesday
Contact: Office 4444 Research II, e-mail: kosecka@gmu.edu, 3-1876
Course web page:http://www.cs.gmu.edu/~kosecka/cs687/

This course will cover several advanced topics in Artificial Intelligence beyond those covered in CS580. These topics will extend existing knowledge about search, machine learning, reasoning and situated action.Topics may include planning, probabilistic reasoning, reinforcement learning, evolutionary computation, neural networks, natural language processing, constraint satisfaction, reactive systems, knowledge-based learning, robotics, vision, emergent behavior, and intelligent multiagent systems. AI is a breadth-oriented field, and the goal of this course is to provide the student with sufficient breadth beyond CS580 to act as a well-versed AI researcher. Informally, CS580 + CS687 typically cover all the topics that might show up on the Ph.D. Qualifying Exam.

The course will comprise of lectures by the instructor, homeworks and presentations of the selected research publications by students. The grade will be based on homeworks and final presentation of the project.

Schedule, Homeworks, Handouts


CS 580, CS480 Artificial Intelligence
Students taking the class should be comfortable with linear algebra, calculus and probability

Required Textbook:

Russel and Norvig: Artificial Intelligence: A Modern Approach, 2nd or 3rd edition
Sutton and Barto: Reinforcement Learning: An Introduction


Homeworks: 35 %
Exam:            35%  (there will be one exam)
Project:          30%
Late policy: Each student will have a 3 day late submission budget, which could be used towards late submssion on the homeworks.

Tentative List of Topics:

  Machine Learning: Classification
 Probabilistic Reasoning, Bayes nets
  Hidden Markov Models, Kalman Filters
  Markov Decision Processes  
  Reinforcement Learning
  Robotics and Computer Vision
  Neural Networks, Support Vector Machines, Ensemble Learning
  Learning Probabilistic Models