Harvard University
3 HN recommenders · recommended in 3 months, 2017–2018
46:29 · 1.8M views · Apr 29, 2013
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 on Ask HN threads as a high-quality free probability course for relearning math or building an ML foundation.
3 comments from Hacker News
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.
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 courses You can get more in-depth knowledge of Machine learning from following sources 1)Nando machine learning course ( [LINK] ) 2)Bishops book ( [LINK] ) Especially Bishops book is really deep and covers almost all basic concepts. Now for recent advances in Deep learning. I will suggest two brilliant courses from Stanford 1) Vision ( [LINK] ) 2) NLP ( [LINK] ) The Vision course by Karparthy can be a very good introduction to Deep learning. Also, the mother book for deep learning ( [LINK] )is good
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 also a good one. It was mentioned here. I specially like the section on theoritical problems, only with exercises on proofs. Every chapter has one. However, Joe Blitzstein problems are more challenging and will train you more on intuition.