Course Description

An introductory course on deep learning methods with applications to machine translation, image recognition, game playing, image generation and more. A collaborative course incorporating labs in TensorFlow and peer brainstorming along with lectures. Course concludes with project proposals with feedback from staff and panel of industry sponsors.

Time and Location

Jan 29 - Feb 2
10:30am-1:30pm, Room 32-123

10:30a-11:15pm: Lecture Part 1
11:15a-12:00pm: Lecture Part 2
12:00pm-12:30pm: Coffee Break
12:30pm-1:30pm: Lab (Hands-On TensorFlow Tutorials)

Project Proposals

Project proposals will be 1-minute pitches of a novel deep learning algorithm, application, open-source contribution, plan to create an interesting dataset, or other contributions. Sponsors will judge and select top projects as award winners. Alternative to project proposal is to submit a 1-page review of an interesting deep learning paper.


If you are an MIT student (undergraduate or graduate) please register by submitting an add-drop form here. You can do this by clicking "create new form" and selecting "Add Drop". Enter the subject information (6.S191) and 3 units when prompted. You can also specify if you want to be registered as a listener or regular student there. If you would like to receive course related updates and lecture materials please sign up for our mailing list.

Grading Policy

3 units P/D/F based on completion of project proposal assignment


Reach out to

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Lecture Schedule

Session Part 1 Part 2 Lab
1 Introduction to Deep Learning [Slides] [Video]
coming soon!
Deep Sequence Modeling [Slides] [Video]
coming soon!
Intro to TensorFlow, Music Generation with RNNs [Code]
coming soon!
2 Deep Computer Vision [Slides] [Video]
coming soon!
Deep Generative Models [Slides] [Video]
coming soon!
Medical Diagnostic from Human X-Ray Scans [Code]
coming soon!
3 Deep Reinforcement Learning [Slides] [Video]
coming soon!
Open Challenges and New Frontiers [Slides] [Video]
coming soon!
Work time for paper reviews/project proposals
4 Guest Lecture: Google [Slides] [Video]
coming soon!
Guest Lecture: NVIDIA [Slides] [Video]
coming soon!
Work time for paper reviews/project proposals + Sponsor Booths
5 Guest Lecture: IBM [Slides] [Video]
coming soon!
Guest Lecture: Tencent [Slides] [Video]
coming soon!
Project Proposal Presentations, Judging and Awards

6.S191 Team


Alexander Amini

Lead Organizer


Ava Soleimany

Lead Organizer


Harini Suresh



Lex Fridman


Teaching Assistants


Shraman Chaudhuri


Yilun Du


Abhimanyu Dubey


Jose Javier Gonzalez Ortiz


Alex Lynch


Nikhil Murthy


Faraaz Nadeem


Tim Plump


Neha Prasad


Bhaktipriya Radharapu

If you are interested in joining the course staff, please contact

Guest Lectures


Adversarial Learning for Generative Models and Inference

Aaron Courville, Associate Proffessor - University of Montreal

Aaron Courville is an Assistant Professor in the Department of Computer Science and Operations Research (DIRO) at the University of Montreal, and member of MILA (Montreal Institute for Learning Algorithms). Courville is a co-author of the textbook, Deep Learning, along with Ian Goodfellow and Yoshua Bengio.


End to End Learning for Self Driving Cars

Urs Muller, Chief Architect of Autonomous Driving - NVIDIA

Urs Muller joined NVIDIA early 2015 to build and lead an autonomous driving team that creates novel deep-learning solutions for self-driving cars on NVIDIA's high-performance DRIVE PX platform. Previously, Muller worked at Bell Labs and later founded Net-Scale Technologies, Inc., a prime contractor on several robotics and machine learning DARPA programs.


This class would not be possible without our amazing sponsors! If you are interesting in becoming involved in this course as a sponsor please contact us at