CPSC 515 2020W1 Lecture Schedule

Future lectures are subject to change.
Some readings and/or links may not work on computers outside of UBC. If you are working from home, use UBC's myVPN service. If you still have problems with a link, contact the instructor.
Material from Artificial Intelligence for Robotics is denoted "AIfR".

Introduction

2020-09-08: Course Introduction.
- Preparation: None.
- Lecture recording did not work.

2020-09-14: Bayes filter and histogram localization.
- Preparation: AIfR Lesson 1.
- Recorded lecture and access passcode: 3Dhx+2bp
2020-09-16: Generic Bayes filter and cost of histogram filter implementation.
- Preparation: AIfR Lessons 2 & 3.
- Bayes filter equations and cost analysis from class.
- Recorded lecture and access passcode: S@H0&j&2. The first 37 minutes of lecture are missing because I forgot to turn on the recording. My apologies.
2020-09-21: Kalman filter.
- Preparation: AIfR Lesson 4.
- Kalman Filter slides from Stephen Boyd's EE363 Linear Dynamical Systems course at Stanford.
- Implementing KF with Python's NumPy library: Colaboratory notebooks with problem definition and starter code and solution. Do not look at the solution until you have attempted to solve the problem from the starter.
- Recorded lecture and access passcode: iWr2G%*g. (I forgot to turn off the recording during the ~20 minute breakout near the end.)
2020-09-23: Kalman filter complexity, limitations, and extensions.
- Preparation: AIfR Lessons 5-7.
- Kalman filter slides from the PR textbook. We looked at slides 6, 7, 17, 21-27, 29, 40-43, 55, 57.
- Recorded lecture with passcode ?4hyVl!&.
- KF Homework: Jupyter notebook or on Colaboratory.
  - Homework is due Friday October 2.
  - Submission instructions will be posted soon.
2020-09-28: Particle filter.
- Preparation: AIfR Lesson 8.
2020-09-30: More particle filter.
- Preparation: AIfR Lessons 9-11.