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 a project proposal competition with feedback from staff and panel of industry sponsors. Prerequisites assume 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: Software Labs


Course Schedule

(2020 schedule coming soon!)
Intro to Deep Learning
Lecture 1

[Slides] [Video] coming soon!

Deep Sequence Modeling
Lecture 2

[Slides] [Video] coming soon!

Intro to Tensorflow;
Music Generation
Lab Session 1

[Code] coming soon!

Deep Computer Vision
Lecture 3

[Slides] [Video] coming soon!

Deep Generative Modeling
Lecture 4

[Slides] [Video] coming soon!

De-biasing Facial Recognition Systems
Lab Session 2

[Code] coming soon!

Deep Reinforcement Learning
Lecture 5

[Slides] [Video] coming soon!

Limitations and New Frontiers
Lecture 6

[Slides] [Video] coming soon!

Pixels-to-Control Learning
Lab Session 3

[Code] coming soon!

Guest Lecture
Lecture 7

[Slides] [Video] coming soon!

Guest Lecture
Lecture 8

[Slides] [Video] coming soon!

Final Projects
Lab Session 4

[Code] coming soon!

Guest Lecture
Lecture 9

[Slides] [Video] coming soon!

Final Project Presentations
Lecture 10

[Slides] [Video] coming soon!

Awards Ceremony
Lab Session 5

[Code] coming soon!

Frequently Asked Questions

For any other questions please reach out to the course staff at introtodeeplearning-staff@mit.edu.

All listeners are welcome to attend!
  • If you are an MIT student, please formally register as a listener on Websis. for instructions
  • If you are not an MIT student, you can still attend the course without registering. Everyone can also sign up for our mailing list if you'd like to receive class related announcements.
6.S191 is offered as a 3 units course and graded P/D/F based on completion of project proposal assignment. Listeners also welcome!
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.

We are expecting very elementary knowledge of linear algebra and calculus. How to multiply matrices, take derivatives and apply the chain rule. Familiarity in Python is a big plus as well. The course will be beginner friendly since we have many registered students from outside of computer science.

If you would like to receive course related updates and lecture materials please sign up for our mailing list.

All course materials available online for free. 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

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

If you are an MIT student, postdoc, faculty, or affiliate and would like to become involved with this course please email introtodeeplearning-staff@mit.edu. We are always accepting new applications to join the course staff.

This class would not be possible without our amazing sponsors and has been sponsored by Google, IBM, NVIDIA, and Onepanel. If you are interesting in becoming involved in this course as a sponsor please contact us at introtodeeplearning-staff@mit.edu.

To view archived versions of this website from past years please click here for 2019, 2018, and 2017.

6.S191 Team

amini

Alexander Amini

Lead Organizer
Instructor

asolei

Ava Soleimany

Lead Organizer
Instructor

Teaching Assistants

riyadh

Riyadh Baghdadi

kristian

Kristian Georgiev

shinjini

Shinjini Ghosh

hunter

Hunter Hansen

alana

Alana Marzoev

julia

Julia Moseyko

jacob

Jacob Phillips

roshni

Roshni Sahoo

gilbert

Gilbert Yang

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