Description
This course will introduce students to the probabilistic and statistical models at the heart of modern artificial intelligence. Specific topics to be covered include: probabilistic methods for reasoning and decision-making under uncertainty; inference and learning in Bayesian networks; prediction and planning in Markov decision processes; applications to intelligent systems, speech and natural language processing, information retrieval, and robotics.
General Information
Lectures (WLH 2005)
Tue/Thu 11:00a-1:50p
Instructor office hour (CSE 3214)
Mon/Fri 9:00a
Discussion sessions (CSE 2154)
Wed 11:00a (Zhen)
Fri 11:00a (Sahil)
Fri 11:00a (Sahil)
Tutoring hours (CSE 4109)
Mon 11:00a (Sahil)
Thu 2:30p (Zhen)
Thu 2:30p (Zhen)
Name | Office Hours | |
---|---|---|
Zhen Zhai | When? Where? | |
Sahil Agarwal | When? Where? | |
Lawrence Saul | When? Where? |
Homework
Homework
Due Date
Lecture Notes
Lecture Notes
Lecture Date
Jul 21, 2016
Jul 19, 2016
Jul 19, 2016
Jul 14, 2016
Jul 12, 2016
Jul 7, 2016
Jul 7, 2016
Jul 5, 2016
Jun 30, 2016
Jun 28, 2016