Course Description

MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, 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 proposal competition with feedback from staff and panel of industry sponsors. Prerequisites assume elementary calculus (i.e. taking derivatives) 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 27 - Fri Jan 31, 2020
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)
Exact times to be confirmed for 2020.

We were recently featured on MIT News and EECS News!

Grading Policy

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

Registration

Registration opens on Dec 3, 2019 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.

Using Slides & Videos

All course materials are copyrighted. If you are an instructor and would like to use any materials from this course (slides, labs, code), you must add the following reference to each slide:

© MIT 6.S191: Introduction to Deep Learning
introtodeeplearning.com

Lecture Schedule

(2020 schedule coming soon)

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]
Biologically Inspired Learning
[Info][Slides][Code][Video]
Work time for paper reviews/project proposals
5 Learning and Perception
[Info] [Slides] [Video]
Final Project Presentations
[Slides] [Video]
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



We are always accepting new applications to join the course staff. If you are interested in becoming a TA, 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