I’ll start things off with some recommendations of my own aside from Susskind’s Statistical Mechanics:
Domain: Classical Mechanics Link: Lecture Collection | Classical Mechanics (Fall 2011) by Stanford University Lecturer: Leonard Susskind Why? For the same reasons as described in the main part of the post for the Statistical Mechanics lectures — Susskind is great! For whom? This was also an undergrad-level course, so mainly for people who are just getting started with learning physics.
Domain: Deep Learning for Beginners Link: Deep Learning for Computer Vision by the University of Michigan Lecturer: Justin Johnson Why? This lecture series is a dinosaur in the field of deep learning, having been recorded in 2019. It’s possible that better introductory lectures on deep learning have been recorded in the meantime (if so, please link them here!), but when I first got started learning about DL in 2022, this was by far the best lecture series I came across. Many options, such as the MIT 6.S191 lectures by Alexander Amini, involved too much high-level discussion without the technical details, while some others weren’t broad enough. This course strikes a nice balance, giving a broad overview of the methods while still discussing specific techniques and papers in great depth. For whom? Beginners in deep learning looking for a broad introductory course.
Domain: Graph Neural Networks Link: Stanford CS224W: Machine Learning with Graphs | 2021 Lecturer: Jure Leskovec Why? I did my bachelor’s thesis on GNNs and needed a refresher on them for that. I remember looking through multiple lecture series and finding these lectures significantly better than the alternatives, though I don’t exactly remember the alternatives I explored. Leskovec is very highly regarded as a researcher in the field of GNNs and also good as a lecturer. For whom? Anyone who wants an in-depth overview of GNNs and isn’t already specialized in the field.
I’ll start things off with some recommendations of my own aside from Susskind’s Statistical Mechanics:
Domain: Classical Mechanics
Link: Lecture Collection | Classical Mechanics (Fall 2011) by Stanford University
Lecturer: Leonard Susskind
Why? For the same reasons as described in the main part of the post for the Statistical Mechanics lectures — Susskind is great!
For whom? This was also an undergrad-level course, so mainly for people who are just getting started with learning physics.
Domain: Deep Learning for Beginners
Link: Deep Learning for Computer Vision by the University of Michigan
Lecturer: Justin Johnson
Why? This lecture series is a dinosaur in the field of deep learning, having been recorded in 2019. It’s possible that better introductory lectures on deep learning have been recorded in the meantime (if so, please link them here!), but when I first got started learning about DL in 2022, this was by far the best lecture series I came across. Many options, such as the MIT 6.S191 lectures by Alexander Amini, involved too much high-level discussion without the technical details, while some others weren’t broad enough. This course strikes a nice balance, giving a broad overview of the methods while still discussing specific techniques and papers in great depth.
For whom? Beginners in deep learning looking for a broad introductory course.
Domain: Graph Neural Networks
Link: Stanford CS224W: Machine Learning with Graphs | 2021
Lecturer: Jure Leskovec
Why? I did my bachelor’s thesis on GNNs and needed a refresher on them for that. I remember looking through multiple lecture series and finding these lectures significantly better than the alternatives, though I don’t exactly remember the alternatives I explored. Leskovec is very highly regarded as a researcher in the field of GNNs and also good as a lecturer.
For whom? Anyone who wants an in-depth overview of GNNs and isn’t already specialized in the field.