MIT Deep Learning for Self-Driving Cars

Learn Deep Learning from a research scientist at MIT, one the world's most reputable universities. Great collection of courses and lectures, providing informative content and real-world examples.

Created by: Lex Fridman

Produced in 2019

What you will learn

  • Train neural network to successfully drive a car
  • Learn how to make a computer detect human presence and human emotions
  • Q-Learning algorithm on a game of Atari
  • How to apply convolutional and recurrent neural networks to self-driving cars
  • Understand Deep Reinforcement Learning with Robot in the Room example
  • Challenges around Human-Centered Artificial Intelligence
  • Basic concepts of Machine Learning and Deep Learning
  • Much, Much more!

Quality Score

Content Quality
Video Quality
Qualified Instructor
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Course Depth & Coverage

Overall Score : 82 / 100

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Course Description

Collection of MIT lectures on deep learning, deep reinforcement learning, and artificial intelligence taught by Lex Fridman.



    • The instructor is a researcher from one of the most prestigious universities in the world.
    • The concepts are presented in a clear and straight-forward manner.
    • Real-world examples to help you understand how to apply the theory behind Deep Learning.
    • Too many topics covered in one tutorial, only scratches the surface of each.
    • Lacks interactivity which can be inconvenient for learners to easily comprehend key concepts.

Instructor Details

Lex Fridman

Lex Fridman is a research scientist at MIT, working on computer vision and deep learning approaches in the context of semi-autonomous vehicles and more generally human-centered artificial intelligence systems. His work focuses on learning-based methods that leverage large-scale, real-world data. Lex received his BS, MS, and Ph.D. from Drexel University where he worked on applications of machine learning, computer vision, and decision fusion techniques in a number of fields including robotics, active authentication, and activity recognition. Before joining MIT, Lex was at Google working on deep learning approaches to large-scale behavior-based authentication. Lex is a recipient of a CHI-17 best paper award and a CHI-18 best paper honorable mention award.



14 total reviews

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By akaberto on 1/13/2019

I think the problem I have with these courses are that there are already wonderful courses that do their jobs perfectly well. These mashup/greatest hits courses ( perception, nlp and deep RL ) are those where you don't really take much out of the course cause everything is dumbed down.

By alex_raw on 1/13/2019

Well.. What bothers me the most is that the materials taught in this "course" seems to be very basic and introductory, to some point, I feel that there might be no need for having such "course" in such "formal" format at MIT. For many topics and techniques, they were just mentioned on the very surface level.

By Re Dream on 1/19/2019

Incredible, the best ever intro and super content, thanks to Lex and MIT!

By Roland Fernandez on 2/22/2019

A tour de force in the selection, organization, and presentation of an overview of Deep Learning. I really enjoyed it

By MrFurano on 9/20/2017

Excellent introduction into Deep Learning! Thank you so very much Prof. Fridman.

By Wenderson Jnio on 10/5/2019

Since 2017, Lex have improved his lessons spectacularly ! Now (2019), I watch a more fluid video with a feeling that this guy know exactly what his talking without hesitating

By Amit Agarwal on 2/15/2019

Thanks for providing some of the clearest and most straight forward introductions to these ML topics.

By Yang Peng on 8/20/2018

Great lectures. Very simple, good graphics.

By Dennis Huang on 9/3/2018

One of the best lectures on application oriented deep learning! Top notch mind and speaker!

By Raymond on 9/16/2018

The way Lex speaks is very powerful. Always to the point and every sentence have a meaning and purpose with a clear voice.

By csankar69 on 8/12/2017

Very poor and hand wavy explanation on some of the key concepts. Not at all clear what the deep Q learning loss function is and why it is chosen that way and how it is evaluated.

By NightLurk on 9/3/2017

Am I the only one that finds the explanations to be quite cumbersome and not easily digestible!? I'm having a hard time following some things, gotta pause, go back, rewatch segments, speculate on a lot of things and extrapolate on speculations then rewatch hoping to match speculations on stated facts to confirm my understanding is correct. I'm not an expert in teaching nor am I a genius but when the lesson leaves so many loose ends and raises more question than it answers, it might not be properly optimized for teaching.