#26 overallClassic
Stat 110: Probability
Harvard University
18 recommenders · 18 threads · recommended in 11 years, 2013–2025 · score 24.2
Harvard's introductory probability course taught by Joe Blitzstein, covering probability basics and problem solving, with video lectures, problem sets, and a companion textbook. Lectures and materials are freely available online and it is also offered on edX.
Commenters call it an excellent, clear introduction to probability with strong intuition and well-regarded lectures from Prof. Blitzstein.
Recommendations by year
20062026
Highly recommend Stats 110 from Blitzstein. Lectures and textbook are all online https://stat110.hsites.harvard.edu/
Stat110[0] (Harvard) on EdX! The professor, Joe Blitzstein, is incredible. Easily one of the best classes I have ever taken.
[0] https://www.edx.org/course/introduction-to-probability
A couple recommendations piggybacking off of yours:
A First Course in Probability has a lot of problems (with solutions) and worked examples, but it’s light on intuition and pedagogy. It’s not an easy book to learn from, on its own. I highly recommend listening to Joe Blitzstein’s STAT 110 lectures and reviewing the wealth of problems/notes. The greater mastery of probability theory that you have, the easier studying ML and stats is. https://projects.iq.harvard.edu/stat110/home
Elements of Statistical Learning is a true textbook—a comprehensive bible that could occupy you for many thousands of hours. ISLR is the better book for a crash course: http://faculty.marshall.usc.edu/gareth-james/ISL/
There are also lectures and slides from the authors: https://www.dataschool.io/15-hours-of-expert-machine-learnin...
I would like to add 'Introduction of Probability' by Joseph K. Blitzstein, Jessica Hwang. I actually have not used the textbook yet but I have taken Prof. Blitzstein's Harvard Stat 110 and it has been the best of all online courses I have taken for statistics. I have also heard very good things about the book. Worth checking out.
The problem sets for the course are available free online. So you can have a look at them to decide.
it’s been a while since I’ve been a beginner so I might not have the best resources, but I would recommend Harvard’s Stat 110 with Joe Blitzstein (lectures online) and then Machine Learning by Kevin Murphy. might be a scarier book to someone not confident in their math, but overall a better one imo
for something more directly comparable to the niche ISLR filled, Bishop’s books are generally better - although I can’t recall their title
Try these:
An interactive website https://seeing-theory.brown.edu that takes you through some of the most critical probabilities and statistics concepts.
Statistics 110: Probability
If you want a more advanced overview of Probabilities and Statistics, this course from Harvard University is an excellent introduction:
https://www.youtube.com/playlist?list=PL2SOU6wwxB0uwwH80KTQ6...
This is MIT Course 18.06, taught by Professor Gilbert Strang.
https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra...
One of the best linear algebra courses that you’ll ever find.
Essence of Calculus
This is Grant Sanderson’s excellent take on calculus:
https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53...
I've been studying MIT's OCW for Probability with a partner. It's a great course.
https://ocw.mit.edu/courses/6-041sc-probabilistic-systems-an...
We're 4 months in and on Lecture 15. These do take a while. I feel like I'm not getting everything and will need another round. I've been binge watching Stat 110 too:
https://projects.iq.harvard.edu/stat110
"All probabilities are conditional"
I'm a bit disappointed as I didn't see some of my favorites here :D One of them has already been mentioned, but I'll add it anyway.
- Harvard Stat 110: awesome and somewhat challenging lecture series on probability. It goes into all the probability basics, but also goes into problem solving skill very often, so the problem sets tended to be hard as I recall it. But the nice thing is that a lot of it you can find the solutions which are very well written -- and for the exams as well. Also, the lecturer Joe Blitzstein won best professor at Harvard if I'm not mistaken. https://www.youtube.com/playlist?list=PL2SOU6wwxB0uwwH80KTQ6...
- Statistical Rethinking by Richard Malkreath: man this one will make you relearn statistics. And with a heavy bayesian flavor, which if you hadn't had the chance to learn, will bend your mind as well. You will learn to build models that can describe a lot of situations in the real world, and estimate the parameters from data. Cool stuff if you ask me. https://www.youtube.com/watch?v=BYUykHScxj8&list=PLDcUM9US4X...
- Frank Harrel's Bioistatistics for biomedical reasearch: Frank Harrel is the go to guy to understand how to use data in clinical trials and diagnostics research. His book Regression Modeling strategies is a gem that every data scientist should read. This lecture series is aimed at biomedical researches, ie. people without a strong background in theoretical statistics. In the lectures he talks about the best practices and pitfalls you'll come accross when doing and reading research, and also explain some R code to do a better job. Harrel also wrote some very important R packages i.e. Hmisc and rms. https://www.youtube.com/@bbrcourse6203/videos
- calling bullshit in the era of big data: this is a last year course so it is very laid back in the discussions. I didn't go through the whole thing. But what I watched I remember it was really nice and thought provoking. https://www.youtube.com/watch?v=A2OtU5vlR0k&list=PLPnZfvKID1...
- statistical learning by Hastie and Tibshirani: these are the guys that wrote the two main books on statistical learning. If one wants to get into DS, this is the place to start. https://www.youtube.com/watch?v=LvySJGj-88U&list=PLoROMvodv4...
- Discrete Differential Geometry by Keenan Crane: ok, I didn't see the whole thing, because it was above my understanding. But the graphics and images are so eye catching I almost wanted to just sit there watching. I'm pretty sure this and his computer graphics lectures are aso engaging as hell and hidden gems of the internet. https://www.youtube.com/watch?v=mas-PUA3OvA&list=PL9_jI1bdZm...
The book for this course is a fantastic practical introduction to Bayesian statistical modeling. If you've ever been curious about probabilistic programming give it a shot.
Also if you're rusty in probability and worried about prerequisites, Harvard's Stat110 can serve as a very good foundation (book and lectures are free online).
I would recommend Harvard Stat 110 over MIT's probability courses - https://www.edx.org/course/introduction-to-probability (you can find full lectures on youtube and the book online)
I am doing harvard 110 now (free lectures on youtube) and I am working through his book Introduction in probability.
Maybe it is too basic for you, but It is focused on the intuition part and I can recommend it!
Start small. I would look at Stat 110 from Harvard, Strang's OCW lectures, and the intro calculus MIT course.
When you're done with that, it'll be easy to see where you want to go next.
I second the recommendation for Blitzstein's book and lectures well as the frustration that more of the questions don't have solutions. I enjoyed reading it much more then Sheldon Ross' book, although both books have good problems.
It's called probability theory, but that's not going to resolve these issues.
If you're interested, there's a great 100-level course online from Joe Blitzstein at Harvard: https://projects.iq.harvard.edu/stat110/home
Thank you! I already had Prof. Yaser Abu-Mostafa's course in there too (also amazing), but I will check out Prof. Blitzstein's probability course as well.
You already have Prof. Gilbert Strang's course in there(made me fall in love with linear algebra), I would like to suggest Prof. Joe Blitzstein's course (https://projects.iq.harvard.edu/stat110/home) on probability and Prof. Yaser Abu-Mostafa's course (https://work.caltech.edu/telecourse) on Machine Learning.
Best advice (in general not just this particular problem) I heard was by Joe Blitzstein (Harvard): Practice, practice, practice.
Much of statistics/probability is about pattern recognition, and developing pattern recognition requires lots of practice.
I enjoyed and learned a lot from his Harvard course Statistics 110: Probability
Video Lectures http://projects.iq.harvard.edu/stat110/youtube
I don't find khan academy's videos to be that great to get at the intuition behind probability and stats. It's good for reference and surface explanations.
I recommend Harvard stats 110 youtube videos: https://www.youtube.com/playlist?list=PL2SOU6wwxB0uwwH80KTQ6...
These videos are more focused on probability, but they contain a lot of great intuitions.
If this kind of stuff interests you, you should check out the lecture videos from Harvard's Stat 110: http://stat110.net. Prof. Blitzstein is hilariously nerdy but still has a very clear presentation style (he had kind of a cult following back when I took the course [1]). If video isn't your thing, the article here is basically the first few chapters of the textbook [2].
[1] http://www.youtube.com/watch?v=iAwS7vzvLnY http://www.youtube.com/watch?v=TQvVLhWOiis
[2] http://www.amazon.com/First-Course-Probability-7th-Edition/d...