- Course: Mathematics of Deep Learning
- Lecture: Monday/Wednesday/Friday, 1:50pm – 2:40pm, Engineering Building 1234
- Office hours: Monday – Thursday, 4:00pm – 5:00pm, Engineering Building 2507F
- Topic idea + 1/2 page abstract + preliminary references: February 14, 2020 (1st report) / April 10, 2020 (2nd report) [optional]
Complete reports: February 28, 2020 (1st report) / April 24, 2020 (2nd report) [required]
Formatting and Length
4 typed pages, using LaTeX, and using the NeurIPS style files with the preprint setting. The style files can be downloaded here.
Topic of your choice, must be related to deep learning in some way. Must use at least two sources, one of which must be a recent research paper in the field. For more details, see the syllabus (linked to above).
- [Feb 17] Lecture 16 notes posted.
- [Feb 15] Lecture 15 notes posted.
- [Feb 13] Lecture 14 notes posted and the link to Shahar Kovalsky’s talk posted.
- [Feb 10] Lecture 13 notes posted.
- [Feb 08] Lecture 12 notes and the link to Karianne Bergen’s talk posted.
- [Feb 05] I’ve started the process of grouping the lecture notes together, see the section “Lecture notes grouped by topic!”
- [Feb 05] Lecture 11 notes posted.
- [Feb 03] Lecture 10 notes and the link to Qing Qu’s talk posted!
- [Feb 03] Corrected lecture 09 notes are posted. Thanks to everyone who helped with these!
- [Jan 30] Lecture 08 notes and the link to Murat Kocaoglu’s talk posted!
- [Jan 27] Lecture 07 notes posted.
- [Jan 27] Small update to lecture 06 notes.
- [Jan 26] Lecture 06 notes posted.
- [Jan 23] Link to a video recording of Jose Bento’s talk posted!
- [Jan 23] Lecture 05 notes posted.
- [Jan 23] Small update to lecture 04 notes.
- [Jan 17] No class on Monday, January 20 for Martin Luther King Jr. Day!
- [Jan 15] Lecture 03 posted! It has the complete notes on functional models that we did not finish in class.
- [Jan 15] Small updates to lecture 02 notes. If you’re “???” in footnote 2, let me know!
- [Jan 13] Lecture 01 notes and slides posted.
- [Jan 13] Seminars of interest for report topics posted below!
- [Jan 03] Class will start Friday, January 10. There are no lectures on January 6 and 8!
- lecture 01 + slides
- lecture 02 + slides
- lecture 03
- lecture 04
- lecture 05
- lecture 06
- lecture 07
- lecture 08
- lecture 09
- lecture 10
- lecture 11
- lecture 12
- lecture 13
- lecture 14
- lecture 15
- lecture 16
Lecture Notes Grouped by Topic:
- Prologue: Course introduction (lecture 01)
- Part 1: Background on machine learning and learning theory (lecture 02 – first part of lecture 11)
- Part 2: Artificial neural networks (second part of lecture 11 – ???)
- Part 3: Convolutional neural networks (TBD)
- Part 4: Geometric deep learning (TBD)
- Part 5: Generative models (TBD)
Seminars of Interest
Deep Learning/Machine Learning Seminars
All deep learning/machine learning seminars are at 10:00am and in the CMSE conference room (1502/1503 EB). The talks will be recorded (with the speaker’s permission) and the links will be posted here once they are available.
- January 23: Jose Bento
- Video of Jose’s talk here.
- January 30: Murat Kocaoglu
- Video of Murat’s talk here.
- February 3: Qing Qu
- Video of Qing’s talk here.
- February 6: Karianne Bergen
- Video of Karianne’s talk here.
- February 10: Shahar Kovalsky
- Video of Shahar’s talk here.
- February 17: Benjamin Fish
The CMSE colloquium will start February 24, and will be held every Monday at 4:00pm in the CMSE conference room (1502/1503 EB). Not every talk will be on deep learning, but some may be! The schedule is here.