Sep 01, 2017
Good intro to Bayesian Statistics. Covers the basic concepts. Workload is reasonable and quizzes/exercises are helpful. Could include more exercises and additional backgroung/future reading materials.
Jun 27, 2018
Great course. The content moves at a nice pace and the videos are really good to follow. The Quizzes are also set at a good level. You can't pass this course unless you have understood the material.
创建者 Diogo P•
Jul 19, 2017
Great lectures. The explanation of each topic is extremely clear and avoids excessive mathematical burden. Lectures are short and concise. Quizzes or at least Module Honors could be a bit more challenging, though. It's a great course, anyway. I'll be looking forward to enroll in the next course of the sequence, entitled "Bayesian Statistics: Techniques and Models".
创建者 Lukas S•
Sep 11, 2017
The course itself is wonderful, and the contents are very thoughtfully selected. I'm not a particular fan of the mirror-technique they use to shoot the videos. Basically, Professor Lee stands in front of a mirror and writes onto the mirror with text markers. On the video you see both him, and the text he writes.
His body often covers the text and generally, it is hard to read. Personally, I see no need to see the professor. Rather, I would prefer a note-taking app (white background). There, old formulas could also be replaced by LaTeX text making everything much more readable, plus there would be downloadable lecture slides automatically.
创建者 DR A N•
Sep 04, 2017
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
创建者 Arthur M•
Mar 30, 2018
Very good introduction to bayesian statistics, but I would have liked a bit more written material to complement the videos, who were rather short and fast.
创建者 Michael D•
Sep 11, 2018
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.
Aug 26, 2018
Though Bayesian statistics is not easy, and quite complex when dealing with prior and posterior. This class provides a good overview the the Bayesian statistics.
创建者 Murray S•
Sep 05, 2018
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.
创建者 Marc S•
Oct 10, 2018
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.
创建者 Ekaterini T•
Oct 31, 2018
I found the need to search for most of the material needed to understand the lessons in other sources. Other than than it was a relatively easy class, which covers nearly the basics. This is not a tutorial on Data Analysis on R, although a short introduction is provided.
Nov 10, 2018
It was my first Bayesian course. Good introduction! However more accent should be placed on intuitive understanding rather than mathematical formalism. To be fair that the issue not only with this course, that the issue with 90% of all stat courses/books. As for me, I find mathematical formalism is hard to digest, intuitive understanding should come first ... May be it's just because of my limited knowledge of stats. I'll update my belief once I get better understanding of stats:) Thank you very much Dr Lee!
创建者 Denitsa S•
Nov 20, 2018
What I liked in the course is that it focuses on examples and solving actual problems. The quantity and the quality of the lectures is great, but what I really missed is written lectures where one can always lookup forgotten things or read details etc. Also, one thing that I think might be added easily is a reference to Mathematica and Maple's routines. I'm using Maple and it took some efforts to get on track. And finally, I think that 4 quizes per week is really too much for working people. It's true that the tests weren't that difficult, but it took me about an hour to do each, so I think 30 mins of lectures vs. 4 hours of quizzes is a bit unfair. Of course, my background in statistics is non-existent so it may be that it took me longer than average. But I think the course material could have been spread over say 6 weeks for lighter load on the students. All best to the team!
Sep 30, 2018
Very nice introductory course, practical and to the point. Good starting point for more detailed courses
创建者 Aravind M•
Apr 17, 2019
Good introductory course. Could provide more hands-on examples
创建者 Yahia E G•
May 04, 2019
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)
创建者 Eddie G•
Apr 21, 2019
It would have been better to have more data analysis applications
创建者 Piotr G•
Jun 17, 2019
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.
创建者 Aditya D•
Jul 17, 2019
The course itself is well structured and covers a lot of material.
There are points in the course where the order of reading material and videos needs to be switched. Also, it would help to update some videos with a little more explanation. It appears as if the lecturer is skipping steps.
创建者 George K•
Jul 30, 2019
Really enjoyed the course! Thank you. I would have given a higher rating if: 1) the instructor had spend more time on the intuition underpinning different derivations, 2) provided more context, 3) discussed more examples from practice. However, I am definitely continuing on to "Bayesian Statistics: Techniques and Models"! Thank you once more, team UCSC!
创建者 Jan J•
Aug 28, 2019
Good course, but it could really use some PDFs with lecture notes ( as in contents of videos, not supplementary material).
创建者 Robert G•
Jul 03, 2019
Overall great course, the last part (linear regression) seems somewhat disconnected from the rest of the course.
创建者 Arasch M•
Jul 07, 2019
The course helps in developing a quite sound grasp of the Bayesian approach to the world. The assignments are feasible and help in gaining a deeper understanding of each subject. However there is a caveat: You definitely need to review your math skills before starting this course (esp. calculus, arithmetics and combinatorics) otherwise you'll be struggling with the particularities !
Jul 09, 2019
Most of the stuff is explained quite well and I managed to understand it. I am quite satisfied overall and I am glad I completed the course. The exercises, however, were somewhat boring. I wish there were some optional exercises that are more challenging and require you to solve more realistic problems. I also wish there were more additional materials with more in depth theory and examples of how they use these concepts for solving problems that are actually of some use. I feel like these improvements would make the course much more interesting and engaging.
创建者 Lee V•
Jul 12, 2019
The lectures were good but rattled-along at quite a speed, even with pausing and "rewinding" I still found it difficult to follow, esp towards the end. I think a short explanation at the start of the video explaining what was going to be covered, what its role was and where it fitted into the big picture might have helped (background is UK maths A-level 45yrs ago and a career on the fringes of science)
创建者 Michael M•
Sep 25, 2019
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).
创建者 Thierry C•
Oct 01, 2019
The course was well explained and there were several exercises pushing the learner to understand the logic behind the mathematical concept. I think it is a suitable class for people with already a certain level of statistics knowledge, even though all concepts are well explained.