|
Lec Date |
Lecture Notes |
Topics
Discussed |
Additional
Readings (Optional) |
|
W1 |
Background,
history, AD processing pipeline |
The Truth About Self
Driving Cars by New Mind (YouTube) 2020
Autonomous Vehicle Technology Report by Wevolver Self-Driving
Cars, 2022, Uni Bonn (YouTube) |
|
|
W2 |
L1 Introduction and Overview cont. |
Sensors
and perception, HD maps, HW platforms, SW platforms, V2X communication,
Ethical issues |
|
|
W2 |
Activation
functions, Cross-Entropy Loss, SoftMax, ROC and AUC, NN training issues |
A
collection of the latest machine learning and deep learning courses |
|
|
W3 |
Intro
to CNN, case studies (LeNet, AlexNet, VGGNet, GooLeNet, ResNet...) |
UC Berkeley CS231n
Convolutional Neural Networks for Visual Recognition Introduction
to Convolutional Neural Networks by Animated AI (YouTube) |
|
|
W3 |
Adversarial
attacks |
NeurIPS
2018 tutorial, Adversarial Robustness: Theory and Practice |
|
|
W4 |
Object
detection (R-CNN, Fast R-CNN, Faster R-CNN, Single-Stage Detectors), object
segmentation |
Deep
Learning for Computer Vision, U Michigan, YouTube
playlist |
|
|
W5 |
Route
planning, behavior planning, local planning, Responsibility Sensitive Safety
(RSS) |
Self-Driving
Cars: Planning (Benedikt Mersch 2020) (YouTube) |
|
|
W6 |
PID,
MPC, Udacity racetrack control, PID tuning with twiddle() |
Lesson
15 PID Control, AI for Robotics at Udacity Controlling Self Driving
Cars PID Control tutorial Understanding PID Control,
Part 1: What Is PID Control? By MATLAB |
|
|
W7 |
Markov
Decision Process (MDP), Bellman Equations, Policy Iteration, Value Iteration |
Reinforcement
Learning: An Introduction, Richard S. Sutton and
Andrew G. Barto Reinforcement Learning MOOC by Chris G. Willcocks (based on Sutton & Barto book) |
|
|
W8 |
Q-learning |
||
|
W10 |
Final Exam, Date TBD, Online on ZOOM, Sample Exam Questions |
|
|
*Slides subject to change. Please download the
latest version after class.
*Lectures on Mon & Wed 15:30-17:00 on Zoom https://umu.zoom.us/j/62818040552
*For your reference, you may look at the previous course offerings Fall 2022 and Spring 2021.
|
Assign Date |
Assignment |
Due
Date |
Additional
Readings (Optional) |
|
W4 |
Lab1. Adversarial Attacks on a CNN for Traffic Sign Classification LabX. Bonus project proposal (refer to instructions in Canvas) |
TBD |
Traffic Sign Recognition using PyTorch and Deep Learning UC Berkeley CS231n Python Numpy Tutorial (with Colab) T81
558:Applications of Deep Neural Networks by Jeff Heaton, YouTube
playlist (Good intro to Python, CoLab, Tensorflow, Keras) Deep
Neural Networks with PyTorch at Coursera |
|
W6 |
Lab2. PID Control |
TBD |
|
|
W8 |
Lab3. DQN RL for Highway
Driving, PPTX, PDF, Video
Lecture |
TBD |
|
|
W10 |
LabX.
Bonus project |
TBD |
|
*Students should form teams of 1-3 people each
for the lab sections. The report should contain a paragraph that explains each
team member’s contribution, and each team member should submit the same copy of
report/source code in Canvas.
* Students should form teams of 1-3 people each
for the lab sections. Each team member should submit the same copy of
report/source code in Canvas.