Creator, Instructor, Co-Founder of Udacity
In Artificial Intelligence for Robotics (formerly CS 8803 O01), learn from Sebastian Thrun, the leader of Google and Stanford's autonomous driving team, how to program all the major systems of a robotic car. This class will teach students basic methods in Artificial Intelligence, including: probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics. Extensive programming examples and assignments will apply these methods in the context of building self-driving cars.
At the end of the course, students will leverage what they have learned by solving the problem of a runaway robot that they must chase and hunt down! Students will also be expected to complete six problem sets, and deliver a final project that applies one of the methods learned in this class on a dataset of their choosing.
Students should know Python or have enough experience with other languages to pick up what they need on their own. Check out Udacity's Introductory CS class (in Python) if you'd like some review. Students should also have strong knowledge of probability and linear algebra (see Prof. Thrun's free Udacity course on statistics).
For prospective students who are unsure if their computer science experience provides sufficient background for this course, the questions below will help gauge preparedness. If you answer "no" to any of the following questions, it may be beneficial to refresh your knowledge of this material prior to taking CS 7638:
- Do you have programming experience, preferably in Python?
- Do you have a strong understanding of linear algebra (undergraduate-level)?
- Do you have a strong understanding of probability (undergraduate-level)?
- Have you taken any courses (either from your undergraduate studies or MOOCs) in machine learning, computer vision or robotics?
- 6 Problem Sets
- Project 1: Two mini-projects covering Kalman Filters and PID
- Project 2: Three projects covering Particle Filters, Path Planning/Smoothing and SLAM
- Extra Credit: Extra credit may be awarded by completing Hardware Challenges, participating in Peer Feedback, and for exceptional helpfulness on the class Piazza forum.
- Grades for the projects will be posted to your student account on Canvas.
Assignment Submission and Late Policy
- All assignments are submitted via Canvas.
- No late work accepted. The specific course schedule will be announced by the instructor and/or TA at the beginning of the term.
Required Course Readings
Website readings with an optional textbook supplement listed below:
- Optional enhancement text - Probabilistic Robotics. Sebastian Thrun, Wolfram Burgard & Dieter Fox. MIT Press. 2005.
Minimum Technical Requirements
- Browser and connection speed: An up-to-date version of Chrome or Firefox is strongly recommended. We also support Internet Explorer 9 and the desktop versions of Internet Explorer 10 and above (not the metro versions). 2+ Mbps recommended; at minimum 0.768 Mbps download speed
- Operating system: -PC: Windows XP or higher with latest updates installed -Mac: OS X 10.6 or higher with latest updates installed -Linux: Any recent distribution that has the supported browsers installed
Background Materials on Statistics
- Prof. Thrun teaches a free introductory course on Udacity called Statistics 101.
- If you prefer written material, Think Bayes is available online. It has some great examples and the text is approachable.
Office hours are held weekly via a YouTube streamed Hangout where we answer questions.
All Georgia Tech students are expected to uphold the Georgia Tech Academic Honor Code.