You can download the handouts for the lectures here.

  • Lecture 1 - Introduction
    tl;dr: Course Introduciton and Logistics,
    [notes]

    Suggested Readings:

    • Course Syllabus
    • Thrun Chapter 1
    • Barfoot Chapter 1
    • Computing machinery and intelligence, Turing (2009)
  • Lecture 2 - Probability Review
    tl;dr: Review of Probability Theory - 2.1, and 2.2 from Chapter 2 notes
    [Chapter 2 notes]
  • Lecture 3 - Markov Chains and HMM I
    tl;dr: 2.3, and 2.4 from Chapter 2 notes
    [Chapter 2 notes]
  • Lecture 4 - HMM II
    tl;dr: 2.4 from Chapter 2 notes
    [Chapter 2 notes]
  • Lecture 5 - Decoding, and Viterbi's Algorithm
    tl;dr: 2.4 from Chapter 2 notes
    [Chapter 2 notes] [Class notes]
  • Lecture 6 - Baum-Welch & Intro to Kalman Filters
    tl;dr: 2.5 and Chapter 3
    [Chapter 3 notes]
  • Lecture 7 - Linear State Estimation and Kalman Gain
    tl;dr: 3.2 and 3.3 from notes
    [Chapter 3 notes] [Class notes]
  • Lecture 8 - Markov Decision Process + Kalman Filter
    tl;dr: 3.4 and 3.5 from notes
    [Chapter 3 notes] [Class notes]
  • Lecture 9 - Extended Kalman Filter
    tl;dr: 3.6 from notes
    [Chapter 3 notes] [Class notes]
  • Lecture 10 - Unscented Kalman Filter
    tl;dr: 3.7 from notes

  • Lecture 11 - Particle Filter
    tl;dr: 3.8 from notes
    [Class notes]
  • Lecture 12 - Rigid Body Transformations
    tl;dr: 4.1 and 4.2 from notes
    [Chapter 4 notes] [Class notes]
  • Lecture 13 - Quaternions & Occupancy Grid Mapping
    tl;dr: 4.2, and 4.3 from notes
    [Chapter 4 notes]
  • Lecture 14 - Occupancy Grid Mapping and Sesnor Models
    tl;dr: 4.3 and 4.4 from notes
    [Class notes]
  • Lecture 15 - Dynamic Programming
    tl;dr: 5.1, 5.2 and 5.3 from notes
    [Chapter 5 notes]
  • Lecture 16 - Stochastic Dynamic Programming
    tl;dr: 5.2 and 5.3 from notes
    [Chapter 5 notes]
  • Lecture 17 - Infite Horizon DP, and Bellman Equation
    tl;dr: 5.3 and 5.4 from notes
    [Chapter 5 notes]
  • Lecture 18 - Value Iteration
    tl;dr: 5.4.2 and 5.5 from notes
    [Chapter 5 notes]
  • Lecture 20 - Policy Iteration & Chapter 6 Introduction
    tl;dr: 5.5 from notes
    [Chapter 5 notes]
  • Lecture 20 - Linear Quadratic Regulator
    tl;dr: 6.1 + 6.2 from notes
    [Chapter 6 notes]
  • Lecture 21 - Continuous Time Systems and HJB
    tl;dr: 6.1 + 6.2 from notes
    [Chapter 6 notes]
  • Lecture 22 - Stochastic LQR and Duality of LQR and Kalman Filters
    tl;dr: 6.2.2, 6.2.3, and 6.3 from notes + Optional Material from notes
    [Chapter 6 notes]
  • Lecture 23 - Iterative LQR, MPC, and Imitation Learning
    tl;dr: Chapter 7: 7.1 and 7.2
    [Chapter 7 notes]
  • Lecture 24 - BC-Stochastic Control and Chapter 8 - Policy Gradients
    tl;dr: Chapter 8: 7.3 and Intro to Chapter 8
    [Chapter 8 notes]
  • Lecture 25 - Chapter 8 - Cross-Entropy Methods, and Policy Gradients
    tl;dr: Chapter 8: 8.1 and 8.2 - Cross Entrompy Methods
    [Chapter 8 notes]
  • Lecture 26 - The Policy Gradient; Control Variates; Actor-Critic Methods
    tl;dr: Chapter 8: 8.3 and 8.4
    [Chapter 8 notes]
  • Lecture 27 - Chapter 9 - Q-Learning
    tl;dr: Chapter 9: 9.1 Tabular Q-Learning and 9.2 - Deep Q Networks
    [Chapter 9 notes]
  • Lecture 28 - Chapter 9 - Delayed Targets; Double Q-Learning + Closing remarks
    tl;dr: Chapter 9: 9.2.1
    [Chapter 9 notes]