Course Description

MIT's official introductory course on deep learning methods with applications to machine translation, image recognition, game playing, and more. Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with project proposals with feedback from staff and panel of industry sponsors. Prerequisites assume elementary calculus (i.e. taking derivatives and applying the chain rule) and linear algebra (i.e. matrix multiplication), we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. Listeners are welcome!

Time and Location

Mon Jan 28 - Fri Feb 1
1:00pm-4:00pm, MIT Room 32-123

1:00pm-1:45pm: Lecture Part 1
1:45am-2:30pm: Lecture Part 2
2:30pm-3:00pm: Snack Break
3:00pm-4:00pm: Lab (Hands-On TensorFlow Labs)

We were recently featured on MIT EECS News! Checkout the press release here.

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

Registration opens on Dec 3, at 9am. If you are an MIT student (undergraduate or graduate) please register here. You can specify if you want to take the course for credit or as a listener 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. Listeners also welcome!

Lecture Schedule

2019
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!
De-biasing Facial Recognition Systems
[Code] coming soon!
3 Deep Reinforcement Learning
[Slides] [Video]
coming soon!
Limitations and New Frontiers
[Slides] [Video]
coming soon!
Learning AlphaZero from Scratch
[Code] coming soon!
4 Guest Lecture: TBD
[Slides] [Video]
coming soon!
Guest Lecture: TBD
[Slides] [Video]
coming soon!
Work time for paper reviews/project proposals
5 Guest Lecture: TBD
[Slides] [Video]
coming soon!
Guest Lecture: TBD
[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


Teaching Assistants

Thomas

Thomas Balch

mauri

Maurizio Diaz

Houssam

Houssam Kherraz

julia

Julia Moseyko

felix

Felix Naser

nigamaa

Nigamaa Nayakanti

jacob

Jacob Phillips

rohil

Rohil Verma

yang

Yang Yan

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



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
IBM
Tencent
Tencent
Microsoft