Course Schedule

Date	Topic	Readings	Due Dates (11:59pm)
Mon, Jan 4	Introduction, Successful Robot Systems, Warmup Asssignment Discussion	Chapter 1
Wed, Jan 6	Review: Bayesian Statistics, Bayes Rule, Bayes Filters, basic math!	Chapter 2
Mon, Jan 11	Our first example: Monte Carlo Localization and efficient variants	Chapter 4.1, 4.3; Chapter 8.1, 8.3-8.5
Wed, Jan 13	Discussion class: Interesting data sets for your project; discussion of potential projects		Warm-up Assignment due
Wed, Jan 20	Slides on motion and perception and Kalman Filters 1: Intuition, basic math	Chapter 3.1-3.3
Mon, Jan 25	Kalman Filters 2: Application to Localization and Tracking; Probabilistic models of robotic sensors, robotic motion	Chapter 4 and 5 (okay to skip derivations)	Project proposals submitted.
Wed, Jan 27	Some final thoughts on historgram filters and Binary bayes filters and occupancy grid maps	Chapter 4.2, Chapter 9.1-9.3
Mon, Feb 1	SLAM: Basic problem, Kalman Filter solution, data association	Chapter 10.1-10.2
Wed, Feb 3 Alex Teichman	SLAM: Graphical methods for SLAM and information form of the Kalman Filter; relation to nonlinear convex optimization	Chapter 11	project snippet #1
Mon, Feb 8	SLAM: Particle Filter and Rao-Blackwellization (FastSLAM)	All of Chapter 13, except 13.4 and FastSLAM 2.0	project snippet #2
Wed, Feb 10 Alex Teichman	Exam prep session	Practice Exam, solutions
Mon, Feb 15	(no class, President's day)		project snippet #3
Wed, Feb 17	Exam
Mon, Feb 22	Discrete data association and information form		project snippet #4
Wed, Feb 24 Alex Teichman	Boosting
Mon, Mar 1	Probabilistic Motion Planning and Navigation		project snippet #5
Wed, Mar 3	POMDPs
Mon, Mar 8	Policy search techniques & Course Summary
Wed, Mar 10	Project Presentations
Sun, Mar 14, 23:59pm			Final Project Report (absolutely no extensions)