[256]

I don't know much about AI but for ML specifically Elements of Statistical Learning is fantastic. I find its explanations a lot easier to understand than other resources. I recommend you skim through it to get a taste. Additionally if you prefer lectures ETHZ has recordings of their ML class[1].

The best way to learn the details is of course to read the original papers. This is especially true for following along with the latest developments in deep learning.

[1] https://www.ethz.ch/content/vp/en/lectures/d-infk/2017/autum...

[jphamtastic]

For someone who might want a higher-level primer, Introduction to Statistical Learning is also great.

http://www-bcf.usc.edu/~gareth/ISL/

[infimum]

> Additionally if you prefer lectures ETHZ has recordings of their ML class[1].

I took that class back in 2015. I found that the lectures were sometimes hard to follow, unless you already know the concepts (which creates a bit of a catch-22). For me the most valuable moments were, when Prof. Buhmann got sidetracked by some anecdotes. Would absolutely recommend, but maybe not as a starting point.

[v3gas]

I'm working my way through Elements now. Do you by any chance know of any lectures specifically based on it? And solutions to the exercises there? I have found it hard to find (good) solutions.

[koulvi]

The Machine Learning Course offered by Prof. Ravindran at Indian Institute of Technology (IIT- Madras) uses ESLR. The course is free to enroll and you will have weekly assignments. https://onlinecourses.nptel.ac.in/noc18_cs26/preview

[v3gas]

Thanks!!

[256]

I haven't looked for any lectures because I learn best from reading books. So, I don't know, sorry. I'm interested in a solution set too.

[QasimK]

Does anyone know how Elements of Statistical Learning compares to Introduction to Statistical Learning which is also from the same authors?

[imh]

ISLR is a simplified version of ESL.