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.
创建者 Luka N•
Nov 10, 2019
Too many probability concepts with too little examples and areas where one can apply them. Also, some steps in the computation are skipped which makes it harder for the learner to understand them. I spent hours trying to figure them out and get the result teachers have got on videos.
创建者 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!
创建者 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.
创建者 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.
创建者 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.
创建者 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.
创建者 Bob F•
Mar 11, 2018
This class provided excellent lectures and very instructive programming assignments. I don't think that the material covered is available in any other MOOC. This class is among the very best I've taken, which is saying a lot because they have to compete with Andrew Ng, Geoff Hinton, and Chris Manning - just to mention few! Thanks for all the great work!
创建者 Ayush T•
Aug 24, 2019
It is undoubtedly one of the best course on Coursera that I've come across. This is really well taught and there is a good balance between the theoretical and the practical aspect of the Bayesian Machine Learning. This course is must-do for those who want to do some good projects in the field of Bayesian Deep Learning which is currently a hot topic now.
创建者 Pablo V I•
Jan 13, 2020
One of the most technicals and high-quality MOOCs I have completed. You need prior knowledge about machine learning and bayesian statistics to complete the assignments.
I highly recommend this course for people working in the industry or researchers. If you are looking for a challenging course, this is your choice.
创建者 Kuldeep J•
Apr 04, 2019
Various advanced Machine Learning topics like Bayesian interpretation techniques, probabilistic modelling, variational auto encoders, etc. have been explained in a very intuitive and simple manner. Then the assignments are well designed to make sure one is able to work on the existing packages available.
创建者 Igor B•
Apr 18, 2019
A wonderful course to improve the theoretical understanding of machine learning and recap probability theory. The lecturers did their best to drag the listener through the math of the EM algorithm and more. The transition to Google Colab indeed simplified online work with Jupyter notebooks.
创建者 Thomas F•
Jan 11, 2020
Great introduction to Bayesian Inference. The final project is fun but maybe a little too easy. If you are looking for deeper math understanding, you will need to do more research on your own but the course gives you many references to look into. Definitely a must have!
创建者 Hythem S•
Dec 16, 2017
Excellent course with great theoretical and practical coverage. There aren't many online courses that offer in-depth coverage of Bayesian methods. Keep in mind this is a newer course and there are a few kinks that still need to be ironed out, but the issues are minor.
创建者 Peppe G•
Feb 20, 2018
I really enjoyed the course. The content is very relevant and rigorous. It is proper university level course on Bayesian methods. The lectures were very good at explaining the material and the assignment were enjoyable. Definitely one of the best courses on Coursera.
创建者 Debasish G•
Nov 14, 2019
This is one of the bets and advanced courses on machine learning that I have done so far. Loved the math part and the programming part. This course has the best coverage of expectation maximization algorithms that I have seen so far. Absolutely loved the course.
创建者 Diogo P•
Jan 05, 2018
Great course. The material is explained with great detail, including the respective mathematical proofs. The assignments could be a bit more demanding, though. The instructors support is very good - they usually answer every question in the forum in a few days.
创建者 Sun X•
Jun 13, 2018
Excellent course! I really learned a lot about Bayesian methods, especially EM algorithm, Variational Inference, VAE, but still did not understand LDA, Bayesian optimization well. It will be better to introduce some backgrounds. Thanks for the lecturers!
创建者 Chan H Y•
May 08, 2018
This course requires fairly good mathematics background. Some topics cover in this course are not often being taught (or only taught in advance research courses) in Computer Science or Engineering Department in other Universities
创建者 David G•
Aug 21, 2018
A very good course with lots of challenging but interesting content. Prior knowledge of Statistics and ML is highly recommended or essential prior to starting the course because there is a steep learning curve.
创建者 Liu Y•
Mar 17, 2018
Concise but very informative, challenges not only from knowledge but also from various tools if you've never met them before. Indeed, great course!
创建者 Alex E•
May 09, 2018
Challenging, but well designed course covering cutting edge ML methods. The course assumes high proficency with Tensorflow, Keras, and Python.
创建者 Subhamoy B•
May 20, 2018
I would like to thank the instructors for this great course. This is definitely not an easy course. But the learning has been immense.
创建者 Meng-Chieh L•
Sep 05, 2018
This is a very interesting class and I learned some concepts and techniques that are beneficial to my work as a data scientist.
创建者 Xinyue W•
May 24, 2019
Fantastic contents! It explains a lot of concepts that confused me when I started Bayesian machine learning very well.
Mar 01, 2018
Excellent course. Nice work, lectors.
Interesting approach with reverted video and glass wall for formulas inference.