Time/Location: Monday
7:20-10pm, Innovation Hall 204
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 CS 580. 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, HandoutsPrerequisites:
Required Textbook:
Grading:
Tentative List of Topics:
|
Machine Learning: Classification, Regression
|
|
Probabilistic Reasoning, Bayesian Networks
|
|
Hidden Markov Models, Kalman Filters
|
|
Markov Decision Processes
|
|
Reinforcement Learning
|
|
Robotics and Computer Vision
|
|
Neural Networks, Support Vector Machines
|
|
Learning Probabilistic Models
|