15-780, Spring 2014: Graduate Artificial Intelligence

Course Info

Number: 15-780
Time: MW 1:30-2:50
Classroom: GHC 4303
Instructors: J. Zico Kolter, Zachary Rubinstein
Units: 12
Office hours: Kolter: M 3-4 (GHC 7115), Rubinstein W 3-4 (NSF 1517), Perera: T 1:30-3 (Wean 4220) , R (GHC 4102)
Textbook: Artificial Intelligence: A Modern Approach
Syllabus: syllabus.pdf

Course Description

Artificial Intelligence spans a wide variety of topics at the forefront of computer science research, including areas like machine learning, robotics, planning, computer vision, natural language processing, an many others. This course serves as a broad introduction to many of these topics, but at the graduate level, where students will delve into specific algorithms and applications in significant detail. Topics to be covered include:

  • Search, uninformed and informed
  • Constraint satisfaction and optimization
  • Numerical optimization
  • Machine learning
  • Planning and reinforcement learning
  • Probabilistic reasoning
  • Robot motion planing
  • Scheduling
  • Computer vision
  • Natural language processing
  • Multiagent systems

Many of these topics have entire courses on the subject, so the treatment in this class will be necessarily brief, but the course is ideal for students who would like some exposure to these topics without devoting an entire semester to each one. The class is primarily intended for PhD, Masters, and senior undergraudate students in CS and ECE, though students from other departments are also welcome to enroll, and encouraged to talk to the course instructors to ensure they have the required background.

Course Structure

The course will consist of weekly lectures. Work for the course will include four problem sets (with both written and programming assignments), a midterm example, and a final course project. More details about the assignments and class project will be distributed on the first day of class.