Introduction to Theory and Practice of Bayesian Statistics

Pros:

1. Working on a final portfolio project was fun
2. Not terribly time-consuming if you're comfortable with the pre-requisites (calculus, integrals, and calculus-based probability theory, stats) -- I spent 20 hours which I think is above average because I prefer to be more thorough
3. The TAs seem to be fairly lenient and consistent graders
4. Probably the best TA support team I've seen in 6 OMSCS courses so far (GIOS is a close contender)

Cons: 4) Should fit a bit more material within the topic schedule/assignments to make the course slightly more challenging 5) Should add proctored closed-note exams or quizzes to hold you accountable and force you to study more.

Mixed / advice: 5) TA's notes website -- there's a few questionable code snippets or pieces of material. It's also clear the website was a work in progress. However, I recognize the TAs went way above and beyond their duty to create and update this website which ends up being way more valuable for most students than the lecture videos. For that, Aaron deserves a promotion. 6) I skipped the lecture videos so can't comment on them. I followed another reviewer's suggestion and read Student's Guide to Bayesian Statistics by Ben Lambert -- its fairly approachable, gives good intuition, lines up somewhat with the course schedule, but math-light. Therefore, I definitely recommend reading through Aaron's notes, relevant parts of other textbooks, OH notes as necessary for the assignments and general understanding.

Other than doing the Math for Machine Learning Coursera recently I hadn't done serious Algebra or Calculus for 20 years before taking this class. I learned that being an 'A' student in math 20 years ago is not an informative prior. This was my first OMSCS course, and I think I would have fared better if I had taken ML4T before this one. The programming aspect in the second half of the class was in line with what I was expecting in terms of the level of challenge. Aaron and the other TAs are great for this course. Professor Joseph even has office hours to help students with assignments or questions. The site for the course with all of the examples in PYMC is great compared to if we would have needed to use BUGS. The lectures are not great and didn't help me retain the content. At times I would watch Ben Lambert or Statistical Rethinking Youtube videos which were helpful. If I was trying to prep before the course I would start with Statistical Rethinking. If I was doing this all over again I would absolutely take this course again because it is very interesting content, but I think I would try to take something else to ease myself into the level of math involved in the course. I hope the lectures can be redone in the near future to make that aspect more worthwhile.

I come from an industrial engineering background for my undergrad. Even though I had learned statistics in college it was mostly in classical statistics. The material itself is very interesting and I learned a lot.

However, the format left a lot to be desired and made it really hard to learn. The professor's recorded lectures (esp after the midterm) are in an old language, so the code walkthroughs aren't helpful, and so you have to self-study PyMC by yourself.

Aaron and the TAs are very helpful though, like many others mentioned in their reviews, and definitely make the second half of the class understandable.

Great class: highly recommended. First of all, the subject is essential for anyone who wants to work in stats / modeling. Second, the open book / notes nature of all the assignments means you really have no reason not to do well. Unlike other classes in which success is based on "memorizing the right tricks the exam."

Potential downsides: it really is more of an independent study kind of environment. The lectures are a mess and the TA material is good, but sometimes incomplete. Given modern resources and the open book / open book / notes nature of the assignments (no HonorLock tests at all), this is a great course. Really glad I took it.

I would really recommend taking Bayesian, if you can then yes I would argue before ML and AI. Many topics overlap (for ML - there’s naive Bayes, aic bic etc. for ai - there’s Bayes networks, markov models etc.)

Pros - it’s a must take for any ML student imo - gives a very different and important view on statistics - Bayesian vs frequentist. In previous semesters it was terrible because of the previous programming languages used. Those outdated languages are now replaced with Pymc and the really big advantage I have seen is the TAs are super proactive, helpful and enthusiastic. They really want to help..

Cons - It’s a little too mathy for my liking. I wish the hws and lectures were a little more application focused rather than really diving so deep into the technical math details.

On difficulty - I’d say it’s a medium course. Not as easy as mlt but maybe on par with rait for me. Ymmv depending on your interest and strength in math and statistics. Having said that, if you put in a few hours- it’s really not hard. The TAs basically give out the answers for HWs in OH if you listen carefully and the grading is extremely lenient (the most lenient in OMSCS I have seen)

I am a quant (model developer) working with an investment bank and this course was by far the most useful for me (8th course in OMSCS). If you work in (or plan to work in) finance then I would strongly recommend this course as Bayesian approach is fastly becoming mainstream for predictive modelling since scarcity of data is a real problem and Bayesian approach excels in such scenarios. I loved the course. The TA support provided in this course is the absolute gold standard. Aaron and this course have become synonymous to the extent that hardly anyone even cared to attend the office hour of the prof (LOL). Attending TA office hours (or watching the recording) is a must to properly digest the math heavy concepts provided in the lectures. The recorded lectures are the weakest part of this course as the prof simply reads from the slides. The lectures through the first half of the course are still manageable but they become insufferable towards the second half. However, Aaron's GitHub website and his office hours more than makes up for this deficiency. I also liked the course structure wherein during the first half the emphasis is on understanding the math and solving problems using hand. Post mid sem, you get to use the pymc library to implement the MCMC algorithms to build your models. The pace was decent and you should be able to do well by putting in 12-15 hours of sincere effort every week. I see some criticism about the high weight (35%) given to the final exam which can make or break your grade. The criticism is further compounded by the release of final exam grade just 2 days before the final grade submission date. I suggest TA's look into this and make the project as the last deliverable and final exam as the penultimate one. Overall an excellent course for people interested in modelling related work.

I genuinely suggest people avoid classes(such as this one!) that have no curves and are very heavy on the final exam(35%) in an online program! It is extremely upsetting if things go wrong during the final exam period. I have well over 90% overall in grades other than the final. I have been working hard for the whole semester. However, due to a sudden exacerbation of my personal health, performing well in the final was extremely challenging. This hurts the grade very significantly(you can call it the alphabet before F). For an exam period of 48hr, the grade is screwed. It is impossible to get any chance to make it up.

Although I am vaguely aware of some school policies like reaching out for incompletes or Dean of Students, it is confusing given the nature of this program, which is totally online.

People have complained about other classes in this program. I actually feel they are more predictable with curves and even distributions of grades among the assignments. You may get bad grades during the semester, but you have opportunities to make them up.

If you don't want surprises, I would suggest you think twice before taking this class! I have almost all As for the program and am graduating next semester. This class was 100% not a pleasant surprise.

Content-wise, the videos are extremely outdated, and the TAs did put in efforts to make it work. I really wish the format of the final exam can be updated for this course.

Note: I started this course with some background in Bayesian statistics and prior experience with PyMC, so I had a general idea of what to expect.

As other reviewers have mentioned, the main drawback of this course is the video lectures. The instructor doesn’t explain the concepts particularly well, and the lectures often felt vague and not very helpful. I found myself skimming through the videos, focusing instead on the notes and relying heavily on Aaron’s GitHub repository for solving the example problems. In addition, Bayesian statistics is a standard topic in many university courses, and there are plenty of online resources available to supplement your learning. My advice: download the course material, check the topics covered, and seek additional resources where needed. This approach worked well for me.

The material itself is excellent. The course does a great job of balancing theory and practice. It covers everything from a review of probability and Bayesian conjugacy to MCMC algorithms. On the practical side, it focuses on building and analyzing GLMs using PyMC.

The TAs were incredible—Aaron’s GitHub repository and the Ed discussions were invaluable resources. The TAs were approachable, responsive, and made the learning experience much smoother.

The assignments were both manageable and engaging, and the untimed one-week midterm and final exams felt fair.

Overall, this course is packed with valuable information and tools. I highly recommend it to anyone looking to build a solid understanding of Bayesian concepts and applications.