Manasi Vartak, PhD Student at CSAIL, Maggie Makar, Grad Student CSAIL
Enrollment: Unlimited: Advance sign-up required
Sign-up by 01/10
Attendance: Participants must attend all sessions
Prereq: Basic ML, Python, basic calculus
Machine learning (ML) is clearly the coolest kid on the block right now, and everyone wants to be friends with ML! However, ML as a field has so many areas and sub-areas, and so much jargon that it is hard for a beginner (or even a grad student in ML) to place all of the problems and techniques in context.
This 4-part class will provide brief overviews of diverse ML areas and discussions comparing and contrasting techniques. Each session will consist of MIT grad students giving 1/2 hr talks on particular topics and a discussion putting those topics in context.
Tentative topics per session are:
- Overview of supervised and unsupervised learning
- Inference
- Bayesian Methods
- Neural Nets
Class is geared towards advanced undergraduate and graduate students. It assumes a basic familiarity with ML.
Please sign up here by 1/10: https://goo.gl/forms/ZGAaq3qKtrqATEJC3
Sponsor(s): Electrical Engineering and Computer Science, Student Information Processing Board
Contact: Manasi Vartak, 32-G904B, MVARTAK@MIT.EDU
| Jan/24 | Tue | 03:00PM-05:00PM | 36-156 |
| Jan/25 | Wed | 03:00PM-05:00PM | 36-156 |
| Jan/26 | Thu | 03:00PM-05:00PM | 36-156 |
| Jan/27 | Fri | 03:00PM-05:00PM | 36-156 |
Manasi Vartak - PhD Student at CSAIL, Maggie Makar - Grad Student CSAIL