If any of you are interested in ramping up on Machine Learning and Deep Learning, I am organizing a reading group for Kevin Murphy’s (Google Research Scientist) new book “Probabilistic Machine Learning”
I am organizing this for the Deep Learning Group on Facebook that I administer:
It’s a wonderful opportunity to learn both the foundations and SOTA of Deep Learning.
The official announcement is:
“Probabilistic Machine Learning Reading Group
I am pleased to announce the reading group for Kevin Murphy's new book "Probabilistic Machine Learning: An Introduction". The book is due to be published in February 2022 by MIT Press but a draft copy of the book can be downloaded from the book's website:
The reading group will start the week of NOVEMBER 15TH 2021 and will run for 23 weeks, so that we that we will covering one chapter
each week. The schedule for the reading group can be viewed here:
We will be using Zoom for videoconferencing the presentations and I will be posting a Zoom link each week a day in advance in the announcements
section of the Deep Learning group. The zoom session will be limited to 100 attendees and attendance will be on a first come first served basis. Naturally the presenters and moderators will be guaranteed a spot.
The presentations will last anywhere from 30mn to 1hr depending on the material and the presenter. Presentations will be followed by an optional 30mn discussion and Q/A session. Attendees will be muted during the presentation but will be able to post comments and ask questions in the chat window throughout the presentation.
It is impossible to select a time that will accommodate everybody, so I’ve decided to have the presentations at:
18:00 EUROPEAN STANDARD TIME.
Keep in mind that this is a reading group and a not a course, so you are expected and encouraged to familiarize yourself with the material prior to the presentation and honestly you'll get a lot more out of the presentation.
I expect everybody to abide by a civilized code of conduct and be respectful towards the presenter as well as your fellow attendees.
Finally, we still need presenters for the following Chapters:
Chapter 9: Linear Discriminant Analysis
Chapter 16: Exemplar-based Methods
Chapter 17: Kernel Models
Chapter 22: Recommender Systems
If you are interested in presenting any of these chapters, please PM directly.
This is going to be fun and educational and I look forward to your participation.”
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This looks like a great book, thanks for letting us know about it. And so nice to get a free, draft copy in pdf format.
I’m hoping to read it and see if I can schedule attending the reading group presentations.