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
1:45pm-2:30pm: Lecture Part 2
2:30pm-2:40pm: Snack Break
2:40pm-4:00pm: Software Labs


Course Schedule

New lecture posted every Friday at 1pm EST!!

Intro to Deep Learning
Lecture 1

[Slides] [Video]

Deep Sequence Modeling
Lecture 2

[Slides] [Video]

Intro to TensorFlow;
Music Generation
Lab Session 1

[Code]

Deep Computer Vision
Lecture 3

[Slides] [Video]

Deep Generative Modeling
Lecture 4

[Slides] [Video]

De-biasing Facial Recognition Systems
Lab Session 2

[Code] [Paper]

Deep Reinforcement Learning
Lecture 5

[Slides] [Video]

Limitations and New Frontiers
Lecture 6

[Slides] [Video]

Pixels-to-Control Learning
Lab Session 3

[Code]

Neurosymbolic Hybrid AI
Lecture 7

[Info] [Slides] [Video]

Generalizable Autonomy in Robotics
Lecture 8

[Info] [Slides] [Video]

Final Projects
Lab Session 4

Neural Rendering
Lecture 9

[Info] [Slides] [Video]

ML for Scent
Lecture 10

[Info] [Slides] [Video]

Final Projects and Awards Ceremony
Lab Session 5

[Video] 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!
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. 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 but are copyrighted and licensed under the MIT license. 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:

© Alexander Amini and Ava Soleimany
MIT 6.S191: Introduction to Deep Learning
IntroToDeepLearning.com

All course materials are copyrighted and licensed under the MIT license. 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:

© Alexander Amini and Ava Soleimany
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

r  iyadh

Riyadh Baghdadi

blake

Blake Elias

kristian

Kristian Georgiev

shinjini

Shinjini Ghosh

hunter

Hunter Hansen

konstantin

Konstantin Krismer

alana

Alana Marzoev

julia

Julia Moseyko

jacob

Jacob Phillips

monisha

Monisha Pushpanathan

roshni

Roshni Sahoo

andyshea

Andy Shea

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