Ask HN: Resources to teach myself Statistics? | Hacker News Reader

Ask HN: Resources to teach myself Statistics?

22 pointsrory_isAdonk12 comments

All are welcome, what seems to be in short supply is stuff at either end, the fundamentals and what a master student might encounter.

To briefly explain why I'm asking this, and why the answers may be of value to others in this community:

I'm trying to break into AI, to understand a large amount of the theory you effectively have to be well versed in statistics. I come from an engineering background where it's really more understanding abstract concepts at speed and applying them correctly with frameworks. While I understand a lot can be achieved by going through projects in TensorFlow and doing some Googling, I feel like I don't understand the internals. Thanks for taking the time to read this.

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"𝗘𝗹𝗲𝗺𝗲𝗻𝘁𝘀 𝗼𝗳 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴" 𝗯𝘆 𝗛𝗮𝘀𝘁𝗶𝗲, 𝗧𝗶𝗯𝘀𝗵𝗶𝗿𝗮𝗻𝗶, & 𝗙𝗿𝗶𝗲𝗱𝗺𝗮𝗻

This is one of the clearest and most respected statistics books ever written.

I personally owe the start of my machine learning career to this book!

You will find so many people around the world (online and IRL) who consider this the bible for statistical learning.

It is so readable, and yet filled with gems and insights from one of the world's most preeminent statisticians.

Free PDF: http://ow.ly/v5Uw50obzpO

Datasets & code: http://ow.ly/a8UZ50obzpN

R Code: http://ow.ly/EEdf50kXUBS

Videos: also available at various sources.

For a "Stats 101" experience:

https://www.youtube.com/playlist?list=PL5102DFDC6790F3D0

For Probability Theory:

https://www.youtube.com/playlist?list=PLUl4u3cNGP60hI9ATjSFg...

https://www.youtube.com/playlist?list=PL2SOU6wwxB0uwwH80KTQ6...

Thanks!

Per comment by @ystad above, I'll add that, if you're looking for maths resources beyond just Stats, that same guy from my first link (Professor Leonard) has really good videos on everything from pre-alegbra to differential equations. He is, imo, a really good lecturer and does a great job of explaining things.

His content is a mix of two modes: in one mode, he's actually teaching a college class and just recording the class. In those videos, you get to hear the questions from the students and some more banter back and forth with the class. The other mode is him teaching "to air" so to speak, where it's just him in a room with a camera and a whiteboard. Diffeq is done that way, because he didn't have a Diffeq class scheduled to record. As best as I can remember, this Precalc, Calc I, Calc II, and Calc III series, and that Stats series, are done with a live class. Not sure if that will matter to you or not: I've found his style engaging and enjoyable in either case.

If you want Linear Algebra, people swear by Gilbert Strang's videos on Youtube.

https://www.youtube.com/playlist?list=PLE7DDD91010BC51F8

(There are also videos of Gilbert Strang teaching Calculus)

There's also a series of videos out there from a class called "Coding The Matrix" which was taught at uummm... I don't remember... Brown, maybe? Anyway, the class was based on teaching Linear Algebra from a programming perspective.

https://codingthematrix.com/

Thank you so much for the effort you put into this comment and of course the content.

I agree you'll definitely want to read Elements of Statistical Learning but there are a few more, namely Think Stats and Think Bayes.

Since no one has really said much about Bayes yet, I think it worth mentioning just how useful it is in DS and ML. A Bayesian approach makes a very good baseline and often one that is hard to beat.

If you're not particular fluent with Probability and Statistics now, let me suggest you add in Khan Academy (make sure to pick the CLEP version) and JBstatistics. Khan has the advantage of quizzes (so you're not just kidding yourself that you know the material). JBstatistics has the advantage of really good explanations. You'll probably want to watch Khan at x1.5 speed.

Ian Goodfellow has a nice free book that includes an "applied math and machine learning basics" section that gives a nice overview of linear algebra, probability theory etc that make it a nice starting point. It's also specifically written for software engineers I believe: https://www.deeplearningbook.org/

I think your question is I want to understand the math behind all of this: I would start by doing Andrew Ng's courses on ml and DL at Coursera

Math is wide for AI and moves into multiple disciplines such as calculus, linear algebra.

Once you have done the above courses you can dive accordingly http://sgsa.berkeley.edu/current_students/books/

Thanks!

“All of Statistics” by Larry Wasserman is a very good - if concise - introduction to statistics. Doesn’t require too much background, although the problems can get pretty hairy. Unfortunately, many of the proofs in the field are based on “tricks” that accumulate, which makes the learning curve steeper for people who don’t have as much background in finding/using these tricks.

This is a easy, short and classic read: https://www.google.com/books/edition/Cartoon_Guide_to_Statis...

EDIT: Also very basic. Should have added that.

Manga guide to Statistics

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