Textbook References:
Detailed instructor notes in addition to supplementary reading material from the following books.
(Thrun) “Probabilistic Robotics” by Sebastian Thrun, Wolfram Burgard and Dieter Fox. PDF
(Barfoot) “State Estimation for Robotics” by Tim Barfoot. PDF
(Lavalle) “Planning Algorithms” by Steve Lavalle. PDF
(Sutton) “Reinforcement Learning: An Introduction” by Richard Sutton and Andrew Barto PDF
(d2l) Dive into Deep Learning by Aston Zhang, Zack Lipton, Mu Li and Alex Smola available at https://d2l.ai is a good reference to read about deep learning.
(Russell) “Artificial Intelligence: A Modern Approach” by Stuart Russell and Peter Norvig. PDF
The following books contain some advanced material. You can use it for your own reference and to brush up fundamentals of machine learning and optimization.
“Pattern Recognition and Machine Learning” by Christopher Bishop. PDF
“An Invitation to 3-D Vision: From Images to Models“ by Yi Ma, Stefano Soatto, Jana Kosecka, Shankar Sastry. PDF
“Reinforcement Learning and Optimal Control” by Dmitri Bertsekas. Material
“Feedback Systems: An Introduction for Scientists and Engineers” by Karl Johan Astrom and Richard M. Murray, PDF
(Advanced) “Linear Systems Theory” by João P. Hespanha. Website
(Fairly advanced) “Stochastic Models, Information Theory, and Lie Groups, Volume 1: Classical Results and Geometric Methods” by Gregory Chirikjian. PDF
Almost all coursework can be done using your laptop. We will use PyTorch (https://pytorch.org) and MuJoCo (http://www.mujoco.org) in the later parts of the course for reinforcement learning. If you want additional computational resources, you can take a look at the following.
Free: Google Colab (https://colab.research.google.com) is a very good platform with a good GPU that you can use for most small-scale experiments. Gradient (https://gradient.paperspace.com) is another free tool with more generous compute resources (6-hour timeouts and persistent sessions). If you haven’t used it already Google Cloud Project gives $300 of starter credits (https://cloud.google.com/free ).
Paid: You can also sign up for Google Colab Pro (https://colab.research.google.com/signup) for a very reasonable $10/month to get access to faster GPUs and less restrictive preemption of jobs.
We will be using Python for the course. More details about downloading Python on your personal computer can be found at https://www.python.org/downloads/ . This is recommended. If additional assistance is necessary reach out to your TAs.
Introduction to PyTorch: https://pytorch.org/tutorials/beginner/nlp/pytorch_tutorial.html
Computer Resources at SEAS: http://www.seas.upenn.edu/cets/answers/big-picture.html
Virtual PC Lab: http://www.seas.upenn.edu/cets/answers/virtualLab.html