This class is an introduction to the practice of deep learning through the applied theme of building a self-driving car. It is open to beginners and is designed for those who are new to machine learning, but it can also benefit advanced researchers in the field looking for a practical overview of deep learning methods and their application.
Interested in the class? Here are some things you could do:
- Register an account on the site to stay up-to-date. The material for the course is free and open to the public. If you're an MIT student and would like to get credit for the course,pre-register for it here.
- Join our Slack channel (deep-mit.slack.com). There are two ways:
(a) if you have an mit.edu email, join here
(b) get an invite by clicking here. - Watch the lectures and guest talks (from 2017 and 2018). We'll make videos available a few days after the lecture is given.
- If you have questions, check out the FAQ Google Doc.
- If you're attending, be careful to note the time and location (it changes regularly) in the schedule below.
- Interact with Lex on Twitter, LinkedIn, Instagram, Facebook, or subscribe on YouTube.
- Check out MIT 6.S099: Artificial General Intelligence.
- If you're not attending the last lecture in-person, get the class shirt online: 2018 versionor 2017 version. No money is made on this shirt. The price is set as the minimum amount allowed by Teespring to cover the costs of printing.
Announcements:
- Sun, Jan 15: YouTube video for Lecture 1: Deep Learning is availabe.
- Wed, Jan 10: DeepTraffic 2.0 competition is launched.
- Sun, Jan 7: First class is still on. A little snow and freezing cold is not going to stop us.
Course Information:
- Time/Dates: Every day, 7pm, Jan 8 - Jan 19
- Duration: 60-90 minutes
- Location: MIT, 54-100 (location details) with some exceptions.
- Instructor: Lex Fridman
- Contact: deep...@mit.edu
2018 Schedule of Lectures and Talks
Most (but not all) lectures and talks will be at 7pm in Room 54-100. See below for exact time and location.
Sacha Arnoud
Director of Engineering, Waymo
Deep Learning for Driver State Sensing
[ Slides ] - [ Lecture Video ] (Available Soon)
Oliver Cameron
CEO, Voyage
Previously: Head, Udacity Self-Driving Car Program
Previously: Head, Udacity Self-Driving Car Program
Sterling Anderson
Co-Founder, Aurora
Previously: Director, Tesla Autopilot
Previously: Director, Tesla Autopilot
Team:
MIT 6.S094: Deep Learning for Self-Driving Cars is a course on a cutting-edge research area. The research group behind this course includes:
2017 Lecture Slides and Videos:
- Lecture 1: Introduction to Deep Learning and Self-Driving Cars
[ Slides ] - [ Lecture Video ] - Lecture 2: Deep Reinforcement Learning for Motion Planning
[ Slides ] - [ Lecture Video ] - Lecture 3: Convolutional Neural Networks for End-to-End Learning of the Driving Task
[ Slides ] - [ Lecture Video ] - Lecture 4: Recurrent Neural Networks for Steering through Time
[ Slides ] - [ Lecture Video ] - Lecture 5: Deep Learning for Human-Centered Semi-Autonomous Vehicles
[ Slides ] - [ Lecture Video ] - Extra: MIT Sloan: Intro to Machine Learning (in 360/VR)
[ Slides ] - [ Lecture Video ]
2017 Guest Talks:
Technology, Policy and Vehicle Safety in the Age of AI
Chris Gerdes - [ Talk Video ]
Professor, Stanford
Past, Present, and Future of Motion Planning in a Complex World
Sertac Karaman - [ Talk Video ]
Professor, MIT
From Research to Reality: Testing Self-Driving Cars on Public Roads
CEO, nuTonomy and Research Scientist, MIT
We Only Adopt What We Trust: Policy and the Business of Autonomy
White House Presidential Innovation Fellow, Office of Science and Technology Policy
Thank You
Support for this course was genorously provided by the companies whose logos are shown below. And none of it would be possible without the great community of bright young minds at MIT and beyond.