Spring 2022 Lectures

Introduction

Background on probability

Markov Chains

Class notes chapters 2.3, 2.4

Hidden Markov Models I

Class notes chapters 2.4

Hidden Markov Models II

Class notes chapters 2.4, 2.5

Kalman Filter Background

Class notes chapters 3.1, 3.2

Kalman Filter

Extended Kalman Filter

Class notes chapter 3.6

Unscented Kalman Filter

Class notes chapter 3.7

Particle Filter

Class notes chapter 3.8

Rigid Transforms, Quaternions

Class notes chapters 4.1, 4.2

Drop Period Ends

Occupancy Grids

Markov Decision Processes

Dynamic Programming

Infinite Horizon DP

Midterm

Background on linear control, LQR

Stochastic LQR, LQG

Background on Machine Learning

Last day to withdraw from a course

Iterative LQR, imitation learning

Behavior Cloning

Policy Gradients

Policy Gradients

Q-learning

Offline RL

Special Topics

Project Presentations

Project Presentations