Nov 18, 2017
This course is little difficult. But I could find very helpful.\n\nAlso, I didn't find better course on Bayesian anywhere on the net. So I will recommend this if anyone wants to die into bayesian.
Jun 07, 2019
Excellent course! The perfect balance of clear and relevant material and challenging but reasonable exercises. My only critique would be that one of the lecturers sounds very sleepy.
par Yanting H•
Sep 18, 2018
A very detailed course for someone who wants to strengthen their statistical background.
par Голубев К О•
Oct 19, 2018
Great course with fine lecturers and deep immersion in Bayesian methods
par Dongxiao Z•
Oct 11, 2018
Learned a lot from this course. Thanks!
par Alexander R•
Nov 12, 2018
super helpful and very applicable!
par Anmol G•
Dec 06, 2018
One of the best in-depth course.
Sep 29, 2018
Awesome. Worth it!
par Max P Z•
Apr 02, 2018
Tough but useful!
par Ertan T•
Apr 26, 2018
par Hugo R C R•
Jun 19, 2018
It probably offers the most comprehensive overview of Bayesian methods online. However, it would be nice these methods translate into practical data science problems found in the industry.
par Daniel T•
Aug 06, 2019
The material is good and a lot of effort went into designing this course. Nonetheless, it feels neglected and could use an update.
The presentations are somewhat muddled by notational abuse. Indeed, it's customary to shorthand every distribution as "p" and let the arguments remind you which variable it came from, e.g, p(x|y) is conditional density of variable "X" at x given that "Y" = y. But then "p(a|b)" could be a completely different function corresponding to random variables "A" and "B"; however, you could have a=x and y=b as vectors which amplifies confusion... And when many variables with different ranges are involved and there's no consistency between labels for the variables and labels for their values, one has to spend extra time deciphering the material. Keeping track of the random variables and adopting a more suggestive notation would go a long way. Also, in Bayesian context it helps to avoid the word "parameter" (other than hyper-parameter, maybe), e.g., the weights "w" themselves are just values of a random variable, which is no different than the data generating process or the latent variables.
The programming assignments contain a lot of missing or inconsistent instructions. Be prepared to sift through the forums to find what is really expected or how to fix the issues in the supplied code.
Overall, I get the impression the course is now maintained by the students. It would be nice to see a revision from the instructors.
par Biarnès A•
Apr 18, 2020
This course is pretty challenging in the sens that one really has to put some effort into understanding the materials and completing the programming assinments. But the problem with this course is the level of english of the speakers which is not that high and also the pedagogical aspects. The teachers should put more time into explainings the models and their details. They should also try to rephrase several times or explain things with different angles. It really goes too fast
Mar 24, 2019
Overall it's good. My problem is that most of this material is better suited to lecture notes and not a video. They're forcing it into a video since it's coursera. Couldn't get through a lot of the lectures, used a textbook instead.
par Pengchong L•
Aug 28, 2018
Not very well prepared. Contents are dry and not well illustrated. Failed to explain points that are made in the videos. The lecturers are reading from scripts and look very nervous.
par Artem E•
Jun 03, 2018
Not so good as I thought. Some times is too complicated and dry. Need more balance. I hope, that guys can better. But I want to say thanks to authors. You did a great job! Good luck.
par Aviv B•
Mar 18, 2020
Explanations are very technical and do not develop any intuition as to what the methods are supposed to accomplish.
Nov 08, 2017
it seems that the prof didn't prepare the course well
par Dizhao J•
Aug 08, 2018
very bad Interpretation
par Novin S•
Feb 03, 2020
I really enjoyed taking this course. The quality of lectures and material were really good, and it was advanced topics as promised. The theories were addressed sufficiently with examples from the real world which made the course not only theoretically interesting but also practically applicable and useful. There have been tiny issues here and there, either during the homework assignments or the material but I hope those will be fixed together with new updates to the course to keep it up to date with the state of the art of the research in the field of Machine Learning.
par Mayukh S•
May 11, 2020
This is one of the best courses I've come across in coursera. The topics are covered in detail. The best part are the proof's for every algorithm they use. This helps in developing useful insights which helps in using these algos for other problems. The assignments and quizzes are challenging. They require the learner to read documentations of libraries and try to come to a solution. Everything is not provided as other courses which is a very good things as this is a advanced course and requires learners to put that extra effort. I would highly recommend this course.
par Martin K•
Mar 16, 2018
The course material is very well prepared and self-contained. Derivation of relevant mathematical formulas is done in great detail which was really helpful. If you've read books like Murphy's "Machine Learning - A Probabilistic Perspective" or Bishop's "Pattern Recognition and Machine Learning" then this course should be easy to follow. If not, it is helpful to have one of these books at hand to get a better understanding, as some topics are presented in a rather condensed form. Thanks to the lecturers for preparing this great course. I can highly recommend it!
par Jordi W•
Feb 28, 2019
This is a challenging course, but well worth it! One needs to be able to manage both the lecture content and the practical side of the course, namely the Python modules/environment. The Python ecosystem is developping fast and some modules changed since the assignments have been created. This means that you need to be able handle deprecations within Python modules and your own Python environment if needed. But this is an advanced course, so I think that is fine. Things have been made easier now that the course creaters have moved assignments to Colaboratory.
par Erwin P•
Mar 17, 2019
This course provides a comprehensive overview how Bayes stats can be used in ML. I'm better able to value the different concepts like EM, GP and VAE and put them into perspective. Depending on you previous math and stats skills the assignments can be challenging and it took me some stamina to complete. The "Russian English" is sometimes a bit of a hurdle when watching the videos, but you get used to it. The concepts are well explained and the references to the additional materials useful.
par Marcos C•
Oct 17, 2019
This course was a fantastic intro to modern Bayesian methods. I particularly liked the references to relevant papers and the useful programming assignments.
The only negative I would say with this course (and all the courses in the specialisation) is that there is usually not enough density of people taking the course so the peer graded assignments take ages to be graded. I would recommend that these bits are made optional and don't count towards the final grade.
par Alya S•
Oct 29, 2019
Very well structured and delivered course. The explanations are generally easy to follow and reproduce. Highly enjoyable and instructive. Assignments are relevant. It would have been great to have an assignment for the Dirichlet Allocation this would have improved the overall understanding of the algorithm. Overall very satisfied I took this lecture. Thanks very much to the lecturers.
par VAISAKH S•
Feb 29, 2020
Amazing course with the right balance of mathematics and practicals... I would say a bit more mathy... I took around 10 weeks to complete this 6 week course since I was new to this area... But I would say my understanding has grown to such an extent that I can easily read papers in this area and make my own derivations for approximate inference... Thanks you guys