Course covers the concept in a very simple way. Examples and assignments are very good.

However some of the statements made throughout the lectures needs more explanation , the course did not dedicate any videos to get familiar with terminology related to probability.

Pen hard to see against shirt. Was mildly irritating to wait for prof to write out stuff, maybe prewrite it?

Went too fast forward for me, would've liked complementary optional material, eg extra quizzes, to help understand and get used to the tougher parts.

Great introductions to Bayesian statistics and inference. Quiz is actually not easy just by passively viewing videos, so taking notes during lectures is strongly recommended. Do not be afraid the Honor quiz...they are not so different from the normal ones.

Likely course and practical: it help us to understand some basic notion for bayesian inference. But Some concepts are less clear and I think need more development and explication (like effective sample size, Jeffreys prior). Great job over all.

The lectures were good, but I hope more background materials can be released. Understanding the topics needs a relative solid mathematical background. Although having completed the course, I am still confused about some concepts in this course.

The course was excellent !...Giving a good overview of the basics needed to navigate through this topic. However, it would have been really great if some specific examples with respect to medicine and public health practice were incorporated

I found it to be a solid course. It has given me better grasp of the basics. I also found it a bit dry, and significant time spent on equations rather than high-level understanding. This is fine, as long as you know what you are in for!

Overall the class is great, especially the first two weeks' content is simple and well-explained. But from the week 3 to the week 4, the professor only writes many formula and doesn't provide enough examples to explain those formula.

Very high quality course. Could use some modifications (e.g. few more applied examples for regression using specific priors, MCMC etc.) and implementing some simple metaphors to introduce some topics before jumping into the maths.

Very good course for beginning bayesian inference. The syllabus is easy to follow, but I also think one could benefit even more by complementing the lectures with other sources (books or other youtube explanation)

Short course that's actually mostly theoretical with a bit of R/Excel analysis. This fitted my needs perfectly. My only suggestion is that they should have released more comprehensive notes for the lectures.

The course content is great and provides a good introduction to bayesian statistics. The assignments could be a little more challenging as a lot of the questions require just plugging numbers into formulae.

I could grab the concept of Bayesian statistics but did not find the course fully self-contained. I had to look elsewhere to fully understand details. More complete supplementary material could help a lot.

Good use of R but maybe use the actual coefficient from the equations themselves rather than picking numbers pre-selected which may confuse.

Unable to look at discussion forum without posting myself.

the notes for the lectures are missing.

In my opinion the notes, which includes the video materials could be very useful.

the course was good. I learnt some new concepts in bayesian thinking.

Very clear and informative. Would like a more extensive and combined reference material (PDF, so less need to lookup e.g. definitions of effective sample size for various distributions).

It was a good course for me to get familiar with the new perspective on statistics. Thank you!

Maybe, some extended practice exercise at the end of the course would make it even better)

A good course but neither notes nor lectures were not in much details. But still it was worth my time. I strongly recommend it if you want a subtle introduction to Bayesian Statistics.

Great course (and teacher). Assumes some basic highschool level for math. With experience in frequentist statistics, but not all the distributions this course was "easy" to follow.

Very straight-to-the-point course. Very dense, though, for a newbe in bayesian terms and concepts. But I definitely suggest it to undertand priors and posterior concepts. Thanks!

A great intro to Bayesian analysis and probability distributions. Personally I skipped the Excel content and converted the R code to python, which was itself valuable learning.

I think the course would benefit by recommending a textbook that would supplement the lecture material. It's nice to have a reference to refer to after viewing the lectures.

This is a good course, I've learn a lot about Bayesian statistics with very little prior knowledge about this subject and even about statistics and probability in general.

This is a nice, bite-sized introduction to Bayesian inference. Helpful lecture notes are provided, alongside introductions to practical computations using R and Excel.

A little heavy on the theory for my style of learning - would have appreciated more clear, applied examples in the lectures, but overall a good and informative course