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.
Previously on LessWrong:
Having seen the popularity of those previous initiatives, I thought it would be valuable to have a similar collection of lecture series. Many universities have made some of their lectures freely available online, leaving a lot of options to choose from. Lectures also vary widely in their quality, and I've often found better alternatives to the lectures offered at my universities online. It would thus be great to have a catalogue of lectures that people have found particularly good.
While lectures are usually of secondary importance to textbooks and exercises when forming deep gears-level models of a field, good lectures are still highly useful for building intuition about a subject, and they're also helpful for those seeking a high-level overview of a field without the ambition to become an expert. The Feynman Lectures on Physics is a classic example of a lecture series that serves both purposes. If this post introduces people to a couple of lecture series that they find as good as or better than Feynman's lectures, I'd consider it a great success!
For the submissions, I'll adopt a format similar to the one used in Parker Conley's post on tacit knowledge videos:
Domain: Statistical Mechanics
Link: Statistical Mechanics (Spring 2013) by Susskind Lectures
Lecturer: Leonard Susskind
Why? Susskind is a legendary physicist who is also excellent at conveying physical intuition and presenting topics in a highly engaging way. These lectures on Statistical Mechanics are no exception. I'll note that there are a few nonstandard choices in his approach to teaching the subject, such as not mentioning the concepts of microstates and macrostates at all in the first few lectures. Nevertheless, I liked it a lot overall.
For whom? The lectures were given for an introductory course and predominantly focus on building the right intuitions. I'd thus recommend it for people who don't yet have first-year physics undergrad-level knowledge in statistical mechanics.
Here are the rules:
On my end, I promise to create a section below with your recommendations once a critical mass of them has been posted, and to keep editing the post afterwards as dictated by the volume of new recommendations. Please share your favorite lecture series below!