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-2:40pm: Snack Break
2:40pm-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 3-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 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.

Grading Policy

3 units P/D/F based on completion of project proposal assignment. Listeners also welcome!

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]
De-biasing Facial Recognition Systems
[Code] [News]
3 Deep Reinforcement Learning
[Slides] [Video]
Limitations and New Frontiers
[Slides] [Video]
Model-Free Reinforcement Learning
[Code]
4 Data Visualization for Machine Learning
[Info][Slides] [Video]
coming soon!
Biologically Inspired Learning
[Info][Slides][Code][Video]
Work time for paper reviews/project proposals
5 Learing and Perception
[Info][Slides] [Video]
coming soon!
Final Project Presentations
[Slides] [Video]
coming soon!
Judging and Awards Ceremony

6.S191 Team

Alexander

Alexander
Amini

Lead Organizer
Instructor

Ava

Ava
Soleimany

Lead Organizer
Instructor


Teaching Assistants

Ravi

Ravichandra Addanki

Thomas

Thomas Balch

mauri

Maurizio Diaz

Houssam

Houssam Kherraz

julia

Julia Moseyko

felix

Felix Naser

jacob

Jacob Phillips

Harini

Harini Suresh

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