Course Schedule

Date	Topic	Readings	Level of difficulty	Due Dates (11:59pm)
Tue, Jan 10	Introduction	Chapter 1
Thu, Jan 12	Probabilistic estimation: the basics	Chapter 2
Tue, Jan 17	Class cancelled (instructor in Paris)	Chapter 5, 6, 7.1, 7.2		Written Assignment I
Thu, Jan 19	Particle filters and Monte Carlo localization	Chapter 4, 8
Tue, Jan 24	Kalman filter tracking and localization	Chapter 3.1, 3.2, 7.3, 7.4
Thu, Jan 26	Information, extended, and unscented Kalman filters	remainder of chapters 3, 7
Fri, Jan 27				Written Assignment II
Tue, Jan 31	Information, extended, and unscented Kalman filters	remainder of chapters 3, 7
Thu, Feb 2	Binary bayes filters and occupancy grid maps	Chapter 9
Tue, Feb 7	Simultaneous localization and mapping (SLAM) EKF	Chapter 10		Warm-up Project
Thu, Feb 9	Particle filters in SLAM	Chapter 13		Project Proposal
Tue, Feb 14	Graphical methods in SLAM and lazy data association	Chapter 11, 12
Thu, Feb 16	Midterm Practice
Fri, Feb 17				Written Assignment III
Tue, Feb 21	Thin Junction trees and SLAM	tba
Thu, Feb 23	Midterm exam	Chapters 1-13 of the textbook
Tue, Feb 28	Markov Decision Processes	Chapter 14
Thu, Mar 2	Partially Observable Markov Decision Processes (1)	Chapter 15		Interim Project Report
Tue, Mar 7	Robot exploration	Chapter 17
Thu, Mar 9	Policy search techniques (Prof. Andrew Ng)	tba
Tue, Mar 14	class canceled (instructor at faculty promotion meeting)
Thu, Mar 16	Student project reviews (long session)
Mon, Mar 20				Final Project Report