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, MIT Room 32-123

10:30am-11:15pm: Lecture Part 1
11:15am-12:00pm: Lecture Part 2
12:00pm-12:30pm: Snack Break
12:30pm-1:30pm: Lab (Hands-On TensorFlow Labs)

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.

Registration

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

Questions?

Reach out to introtodeeplearning-staff@mit.edu

To view archived versions of this website from past years please click here.

Lecture Schedule

Session Part 1 Part 2 Lab
1 Introduction to Deep Learning [Slides] [Video] Deep Sequence Modeling [Slides] [Video] Intro to TensorFlow, Music Generation with RNNs [Code]
2 Deep Computer Vision [Slides] [Video] Deep Generative Models [Slides] [Video] Disease Detection from Human X-Ray Scans [Code]
3 Deep Reinforcement Learning [Slides] [Video] Limitations and New Frontiers [Slides] [Video] Work time for paper reviews/project proposals
4 Guest Lecture: Google [Info] [Video1] [Slides2] [Video2] Guest Lecture: NVIDIA [Info] [Slides] [Video] Sponsor Booths + Work time for paper reviews/project proposals
5 Guest Lecture: IBM [Info] [Slides] [Video] Guest Lecture: Tencent [Info] [Slides] [Video]
coming soon!
Project Proposal Presentations, Judging and Awards

6.S191 Team

Alexander

Alexander Amini

Lead Organizer

Ava

Ava Soleimany

Lead Organizer

Harini

Harini Suresh

Co-Chair

Lex

Lex Fridman

Co-Chair


Teaching Assistants

Thomas

Thomas Balch

shraman

Shraman Chaudhuri

yilundu

Yilun Du

Abhi

Abhimanyu Dubey

Jose

Jose Javier Gonzalez Ortiz

ajlynch

Alex Lynch

faraaz

Nikhil Murthy

faraaz

Faraaz Nadeem

tim

Tim Plump

bhakti

Bhaktipriya Radharapu

If you are interested in joining the course staff, please contact introtodeeplearning-staff@mit.edu



Guest Lectures

Courville

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.

Sponsors

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 introtodeeplearning-staff@mit.edu

NVIDIA
Google
Tencent
IBM
Microsoft