This looks like a fantastic resource. Thanks for sharing!
I really enjoy the Bayesian side of ML, but it's definitely not the most accessible. Erik Bernhardsson cites latent dirichlet allocation as a big inspiration behind the music recommendation system he originally designed for Spotify, which is apparently still in use today[1]. I still struggle with
grokking latent factor models, but it can be so rewarding to build your own
and watch it work (even with only moderate success!).
Kevin Murphy has been working on a new edition of MLaPP that is now two volumes, with the last volume on advanced topics slated for release next year. However, both the old edition and the drafts for the new edition are available on his website here[2].
The University of Tübingen has a course on probabilistic ml which probably has one of the most thorough walkthroughs of a latent factor model I've found on the Internet. You can find the full playlist of lectures for free here on YouTube[3].
In terms of other resources for deep study on fastinating topics which require some command over stats and probability:
- David Silverman's lectures on reinforcement learning are fantastic [4]
- The Machine Learning Summer School lectures are often quite good, with exceptionally talented researchers / practictioners being invited to provide
multi-hour lectures on their domain of expertise with the intended audience being
a bunch of graduate students with intermediate backgrounds in general ML topics. [5]
I really enjoy the Bayesian side of ML, but it's definitely not the most accessible. Erik Bernhardsson cites latent dirichlet allocation as a big inspiration behind the music recommendation system he originally designed for Spotify, which is apparently still in use today[1]. I still struggle with grokking latent factor models, but it can be so rewarding to build your own and watch it work (even with only moderate success!).
Kevin Murphy has been working on a new edition of MLaPP that is now two volumes, with the last volume on advanced topics slated for release next year. However, both the old edition and the drafts for the new edition are available on his website here[2].
The University of Tübingen has a course on probabilistic ml which probably has one of the most thorough walkthroughs of a latent factor model I've found on the Internet. You can find the full playlist of lectures for free here on YouTube[3].
In terms of other resources for deep study on fastinating topics which require some command over stats and probability:
- David Silverman's lectures on reinforcement learning are fantastic [4]
- The Machine Learning Summer School lectures are often quite good, with exceptionally talented researchers / practictioners being invited to provide multi-hour lectures on their domain of expertise with the intended audience being a bunch of graduate students with intermediate backgrounds in general ML topics. [5]
1: https://www.slideshare.net/erikbern/music-recommendations-ml... 2: https://probml.github.io/pml-book/ 3: https://www.youtube.com/playlist?list=PL05umP7R6ij1tHaOFY96m... 4: https://www.youtube.com/playlist?list=PLqYmG7hTraZDM-OYHWgPe... 5: http://mlss.cc