syllabus.md

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title	Syllabus
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Course Syllabus

PDF copy of the syllabus PDF{:target="_blank"}

Module 1: State Estimation (11 lectures)

• Background on probability
• Topics: Markov chains, Hidden Markov Models, Kalman Filter, Extended and Unscented Kalman Filter, particle filters, occupancy grids, transformations

Module 2: Control and Planning (5-6 lectures)

• Background on linear control, dynamic programming
• Topics: Markov Decision Processes, Value and Policy Iteration, Bellman equation, Linear Quadratic Regulator, Linear Quadratic Gaussian, Sampling-based motion planning

Module 3: Reinforcement Learning for Robotics (7 lectures)

• Background on deep learning and optimization
• Topics: Imitation Learning, Policy gradient, Q-Learning, Inverse RL, Model-based RL, Offline RL

Module 4: Miscellaneous topics (2 lecture)

• Meta-Learning, Sim2Real

Tentative schedule

Lecture	Topic	Notes
1	Introduction	HW 0 out (not graded)
2	Background on probability	HW 1 out
3	Markov Chains
4	Hidden Markov Models I
5	Hidden Markov Models II
6	Kalman Filter	HW 1 due
7	Extended Kalman Filter
8	Unscented Kalman Filter	HW 2 out
9	Particle Filter
10	Rigid Transforms, Quaternions
11	Occupancy Grids	Summary on Lec 4-10
12	Dynamic Programming, Bellman Equation	HW 2 Due
13	Value Iteration
14	Policy iteration	HW 3 out
15	Background on Linear Control, LQR
16	LQG, Iterated LQR
17	Midterm
18	Sampling Based Motion Planning	HW 3 Due
19	Optimization, Imitation Learning
20	Policy Gradient
21	Tabular Q-Learning
22	Continuous Q-Learning	HW 4 out
23	Inverse RL, Model-based RL
24	Offline RL
25	Deep RL	HW 4 due
26	Closing topics