#64 overall
6.041 Probabilistic Systems Analysis and Applied Probability
MIT OpenCourseWare
6 recommenders · 6 threads · recommended in 5 years, 2014–2023 · score 7.9
An MIT undergraduate course on probability taught by John Tsitsiklis, covering probabilistic modeling, random variables, limit theorems, Bayesian inference, and Markov chains. Lectures are available free through MIT OpenCourseWare and YouTube.
Commenters call it a very good, amazing course for learning probability from the ground up, and one recommends it alongside Tsitsiklis' book as superb material.
Mathematicsintermediatefreevideo lectures#probability#statistics#markov chains#bayesian inference#tsitsiklis
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20062026
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"
yeah, i am about to wrap up the MIT probability course. Very very good course, i will be happy if i retain 50% of what i learned. Markov Chain is my favorite so far.
Why tech might be better. At best: Immediate direct feedback to a student when doing exercises that consolidate understanding. Video is a powerful tool, animated explanation, difficult demonstrations, historical footage are all available and you need get the delivery right once and it scales up to millions of students from there. Psych tricks along the gamification line can be useful in providing additional motivation.
MIT's probability course is amazing. So it can be done, for a given audience at least.
I have my doubts about a computer convincing someone who lacks confidence that they are easily smart enough to excel at this task or learning in general.
What do you use a screen for? YMMV. If they're ever going to work in a school some teachers are going to be doing amazing things to make that happen. How would they know what to do? Easy to see good teachers failing at that too.
To iterate on what others said, but what was not emphasized enough from my point of few:
AI is academic (as a synonym for 'theoretical' and 'math-intensive'). Once you look beyond purely symbolic AI, which proved to be infeasible as @curuinor pointed out somewhere here, you will need to build up at least basic knowledge in probability theory and linear algebra.
The path I'm following at the moment is a quite rigorous one and is outlined here (http://www.deeplearningweekly.com/pages/open_source_deep_lea...).
If you've never had any exposure to probability theory or statistics, I recommend having a look at the course "MIT 6.041 Probabilistic Systems Analysis and Applied Probability" taught by John Tsitsiklis at MIT (video lectures are available through YouTube and MIT OpenCourseWare for free). Both the course and Tsitsiklis' book are superb learning materials to get into probabilisitc thinking.
Edit: Link was broken. Thanks to @blauditore.
MIT 6.041[1] is a very good course I can recommend. Not sure if MITx 6.431x on edx is the same, but it's the same teacher in any case.
[1] https://www.youtube.com/watch?v=j9WZyLZCBzs&list=PLUl4u3cNGP...
Expanding on my previous comment (https://news.ycombinator.com/item?id=8444705), here are some excellent, tried-and-tested resources to help you learn the fundamentals.
1. Probability and stats: For probability, work through John Tsitsiklis's MIT OCW course 6.041. There is also an archived version of the course available on EDX. Norman Matloff's book (http://heather.cs.ucdavis.edu/~matloff/132/PLN/ProbStatBook....) approaches stats from a CS practitioner's point of view (lots of sample simulation code in R), and has intuitive explanations of many different statistical concepts. For Hypothesis testing, checkout the courses Mathematical Biostatistics - 1 and 2 on Coursera.
2. Linear Algebra: Gilbert Strang's OCW lectures / course notes is hands down the best resource. But depending on your mathematical background, the lectures can be a bit long-winded.
3. Programming proficiency: For algorithms, Robert Sedgewick's algorithms course on Coursera has excellent videos. His textbook is very well written too. I really like his style of writing actual working code for each algorithm he describes (in contrast to say, CLRS): i find his code samples rather elegant and worthy of study. For learning object orientation and design patterns, the best way is to write large programs and read the book "Head First Design Patterns" as you do so.
The aim is to develop an ability to think with a statistical / algorithmic lens. Many real world phenomena are stochastic in nature. Modelling them by simulation and measuring and drawing confidence intervals around your measurements must become second nature. When confronted with an image / text processing problem, representing you data as vectors and manipulating them must be natural. While writing large programs, being aware of design considerations such as preferring composition must come naturally to you. Of course, you should be able to always recognize when a computation is O(n^2) vs O(n log(n)) vs O(2^n).
Once you are done with that, all you need to do is work through Andrew Ng's Machine Learning course and compete in a few Kaggle competitions / build some ML related side projects. Congratulations, you are now a data scientist good enough to work at most places in the tech industry!;)
You can take free online courses to refresh your Statistics knowledge.
Coursera https://www.coursera.org/browse/data-science/probability-and...
Coursera - Making sense of Data http://academictorrents.com/details/a0cbaf3e03e0893085b6fbdc...
MIT 6.041 Probabilistic Systems Analysis and Applied Probability https://www.youtube.com/playlist?list=PLUl4u3cNGP61MdtwGTqZA...
Statistics 110: Probability - Harvard https://www.youtube.com/playlist?list=PL2SOU6wwxB0uwwH80KTQ6...
Udacity also has few courses on Statistics.