Lecture 1: Probability and Counting | Statistics 110
Why Hacker News recommends it
Recommended on Ask HN threads as a high-quality free probability course for relearning math or building an ML foundation.
What it is
Lecture 1 of Harvard's Statistics 110 with Joe Blitzstein, introducing sample spaces, the naive definition of probability, counting, binomial coefficients and the sampling table.
Recommended by 3 HN commenters across 3 different months, 2017 to 2018.
What Hacker News said
Strong rec pixelperfect · 2017-05 · on “Ask HN: What's the best way to relearn math?”
Here are some excellent YouTube playlists for the subjects you mentioned. I have used and can vouch for the high quality of all of them: Linear Algebra: [LINK] Probability: [this video] Calculus: [LINK] Be sure to work through problems and not just watch lectures, because otherwise you'll forget the content. For example, the linear algebra course has a companion site with problems, and the professor of the probability course wrote an excellent book with problems.
Recommends neel8986 · 2018-01 · on “Ask HN: How to Seriously Start with Machine Learning and AI”
This is kind of a masters degree course i created for myself to get knowledge of Machine Learning from bottoms up First, you need a strong mathematical base. Otherwise, you can copy paste an algorithm or use an API but you will not get any idea of what is happening inside Following concepts are very essential 1) Linear Algebra (MIT [LINK] ) 2) Probability (Harvard [this video] ) Get some basic grasp of machine learning. Get a good intuition of basic concepts 1) Andrew Ng coursera course ( [LINK] ) 2) Tom Mitchell book ( [LINK] ) Both the above course and book are super easy to follow. You will get a good idea of basic concepts but they lack in depth. Now you should move to more intense books and
Recommends pedrodelfino · 2018-11 · on “Ask HN: Book which comprehensively covers probability theory?”
I recommend "Introduction to Probability" by Joseph K Blitzstein and Jessica Hwang. This book is tough. Really tough. However, you are really going to do "deliberate practice" while having a go on the assignments. It bothers me that the author does not provide the answer to most of the questions. This is specially bad in this field of Mathematics (Probability). As an example, in calculus you can always plot the curve, the derivative and see if the result makes sense, in probability theory it is hard to have a simple and safe sanity check. You can watch all classes from Harvard here for free: [this video] If you want, there is also a MOOC in EdX with the same material: [LINK] Sheldon Ross' book is