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Lecture 1: Probability and Counting | Statistics 110

Harvard University

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  • Recommended by 3 people: 3 on Hacker News.
  • Presented by Joe Blitzstein
  • May be out of date. Uploaded 13 years ago, in an area where tools change fast.

Beginner. No background in the topic needed.

1.8M views13 years ago3 recommendations

Computer scienceAI and machine learningMath for programmersMath for machine learning

Joe Blitzstein introduces sample spaces, the naive definition of probability, and fundamental counting rules including binomial coefficients.

We introduce sample spaces and the naive definition of probability (we'll get to the non-naive definition later). To apply the naive definition, we need to be able to count. So we introduce the multiplication rule, binomial coefficients, and the sampling table (for sampling with/without replacement when order does/doesn't matter).

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3 people recommend this

What people say. Highly recommended as part of an excellent foundational course on probability for computer science and machine learning.

pixelperfecton Hacker News • Recommends

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: link to 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.
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neel8986on Hacker News • Recommends

On “Ask HN: How to Seriously Start with Machine Learning and AI”

…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 link to 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 g…
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pedrodelfinoon Hacker News • Recommends

On “Ask HN: Book which comprehensively covers probability theory?”

…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: link to 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.…
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