PMR2728/5228 - Probability Theory in AI and Robotics
Escola Politecnica, USP
Fabio G. Cozman
The objective of the course is to present a self-contained
description of probabilistic techniques that have had a
significant impact on the fields of artificial intelligence
and robotics.
The course consists of 12 classes, each lasting 3 hours.
The first two classes are mostly devoted to basic theory,
with forays into history and interpretation.
The remaining classes are generally divided into a first
block with theory, a second block with applications, and
a third block on advanced concepts.
The syllabus contains
the sequence of topics discussed in class.
- Commented syllabus.
Link to paper
In defense of probability,
by Peter Cheeseman.
- Historical review.
- Basics on probability theory
(finite spaces, subjective perspective).
Support text and exercises.
- Bayes nets and Markov random fields,
Part I and
Part II.
Link to nice material on graphical models (support).
- Infinite spaces.
- Decision making.
Related exercise set.
- Basics on machine learning and
classification.
- Bayesian network learning.
- Markov Chain Monte Carlo methods.
- Sequential decision making.
Page updated by Fabio Cozman (21/10/2013).