Very informative as an introduction to concepts, but nowhere near the deep dive I'm now interested in taking.

Good course!!... Additional examples of real life explained and done in R or excel will make it great

A lot of formulas and not that much interpretation. It is a good start in Bayesian concepts.

Too much theoretical than practical applications. No need to give both R and Excel videos.

Would have liked more problem solving and real-world application examples.

The workload is manageable however the homework is somewhat challenging.

Not well organized.

No sufficient materials, references, etc.

Very short.

Overall, it's Ok. but the explanation is too short and incomplete.

better to come up with more examples and more mathematical details

This course could be taught in better understanding way

For some derivations, the explanations are too sparse.

needed a list of textbooks and references...

thank you my teacher

A bit too short.

I expected better teaching quality. The instructor is undoubtedly one of the bests in his area, but I personally did not like his teaching in this course. I felt he knows a lot of interesting concepts but intentionally does not teach them. The whole course was like somebody was reading from a textbook without adding any comments for students to actually grasp the concepts. In general I liked the course but I expected to learn much more from it.

It's alright because it gives you an overview of what is covered in a Bayesian Stats class, but the material is presented quite poorly and I had to do a lot of second hand reading to answer the questions. It is not particularly enlightening even and the formulas are presented without proper grounding, context, and intuition. I can recommend this only for dedicated self-studiers who already have some sort of grounding in Bayesian reasoning.

Bayesian Statistics is an essential course for anyone working with data. THis course will provide some introductory concepts and proofs. However as the course progresses to week 4 explanations are rushed or nonexistant.

Compared to other courses like from deeplearning.ai this course is not well maintained with poor quality of instruction and exercises. It feels like a wasted opportunity.

Week 4 explanations are just theoretical where professor is literally not giving any intuition and rushing through the concepts with equations which did not make any sense to me. Till week 3 I could kind follow. I did this course with the intend of giving a based for Machine Learning study and I am an thoroughly disappointed the way it ended up.

The topic is very important, but the lectures are sometimes like reading wikipedia and rewriting it on the blackboard. It lacks intuitive examples and analogies. It might be OK if somebody already have really strong statistician background and intuition. Otherwise it would be really tough to catch up.

Good content. However, way of presentation is not very engaging. Presenter's voice very monotonous and free of any engagement. In my opinion, scribbling formulas to the board does not make a helpful learning experience.

Unsuficient explanation.

Lecturer just writes formulas without trying to explain background concepts. It is like reading a book of statistics. No way that most of the students will understand it.

Some matters were just given formulas and there was a lack of practice. The course should cover less materials or be longer to be effective in teaching.

Disappointing. Hard to follow, as concepts are not fully explained or linked. Steps in equations are often skipped without notice.

it was an okay course, I liked that they used R occasionally in the course, but I did not like how the concepts were discussed

Not very much in depth and does not offer complete lecture notes, which are necessary for answering the quizzes...