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Bayesian Methods for Machine Learning に戻る

HSE University による Bayesian Methods for Machine Learning の受講者のレビューおよびフィードバック



People apply Bayesian methods in many areas: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to estimate uncertainty in predictions, which is a desirable feature for fields like medicine. When applied to deep learning, Bayesian methods allow you to compress your models a hundred folds, and automatically tune hyperparameters, saving your time and money. In this online HSE course we will discuss the basics of Bayesian methods: from how to define a probabilistic model to how to make predictions from it. We will see how one can automate this workflow and how to speed it up using some advanced techniques. We will also see applications of Bayesian methods to deep learning and how to generate new images with it. We will see how new drugs that cure severe diseases can be found with Bayesian methods. Do you have technical problems? Write to us:



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.


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.


Bayesian Methods for Machine Learning: 151 - 175 / 195 レビュー

by Mauro D S


Hard material, but very well explained. The peer-review exercises are interesting as well, but if the reviewer does not understand the material, I wonder how useful they are. Open research question I guess (i.e. how to make sure the student reviewer understands what he is reviewing-are there any baseline reviews established a student should go through first?)!

by Filip B


The lectures explain complex and useful phenomena. I found it useful to use a whiteboard and try to solve the derivations along with the lecturers. There are some gaps in the lectures. The quizzes and assignments can be solved quite easily by repeated submission. The programming assignments are entertaining and empowering.

by Ivan P


Отличное начало, но середина и конец немного разочаровали. Мне не хватило больше практики в стиле "build <mcmc/gp/ppca/etc> in python from scratch". Понравились выводы у доски. В целом курс хороший, но для освоения программы только курса недостаточно и нужно искать дополнительные материалы, благо это несложно.

by Milos V


As PhD in physics I found lecture super-boring (too much theory and derivation) and irrelevant to the practical assignment. On the other hand, most of practical assignments are explained very pedagogical manner (except week 5!). As for the first course - I would recommend more code-related lectures.

by Alexander E


Excellent material! I got new very useful knowledge. I really like the final project. Although course design is not perfect. It would be great to have additional content (links or documents), lectures are not enough to pass the tests. Also some assigments have issues (code and grader errors).

by 魏力


Good course. But some suggestions: topic about variational inference or variational EM in theory is quite tough, better to have equivalent level of assignment for better practical understanding. Personally, I feel VAE is a very simplified application case.

by Gavin L


Probably the best online course available on this topic and some projects are indeed interesting and worth the investment. However, i don't feel like quizzes and some projects are challenging (or at least worth some effort) to build a solid understanding.

by 冯迪(Feng D


The materials of this lecture are awesome. Very useful! However, the introduction of project assignments are very confusing, especially the final project. It took me hours to understand what the task is really about, and what should we really do.

by Ankit Y


Good course contents with apt assignment and quizzes. Anyone interested in Bayesian approaches should definitely do this course. It has relevant programming assignment exposure to help you in kickstart your work in the said domain.

by Крюков Д О


This is very good course. All the lectures materials were explained at a high level. Unfortunately, I would want more practical exercises for improving formulas derivation skill which is very important for this course.

by Ishaan B


The content+course structure was phenomenal. The assignment environment setup was a bit cumbersome at times, but the level of difficulty in the assignments really solidified the understanding of the course material.

by Guy K


a very important material is covered in a clear manner.

some of the labs could have been more effective (e.g. avoid unnecessary mixing between tensorflow and Keras)

Strongly recommended course ! great curriculum !

by P C


The course covers a lot of very advanced material and is a great starting point for Bayesian Methods, but it would greatly benefit from having additional reading materials.

by Yaser E


I​ enjoyed the course. Very informative and high quality content. I learned a lot about things that are hard or time-consuming to find elsewhere. A fan of HSE!

by Olaf W


Great class. Well presented material. Sometimes the path from introduction to advanced material could use a few steps in between.

by Diego E P M


A very good course on Bayesian methods, though I find explanations are a bit confusing from time to time.

by Saad B


A difficult course. Some derivations were rushed but overall a good course that requires a lot of work

by Chiang y


We may need more help for homework format or quiz answer format. It took me lots time for solving it.

by Maxim V


Amazing course, an absolute must! Only some programming assignments were having minor issues.

by Sai H Y


covering Additional and recent Bayesian methods will make this course exceptional

by Kanhaiya K


Assignments were very helpful in understanding the core concepts

by 洪贤斌


Good course but a bit difficult and the peer review is helpless



Good course.

Too much theory, not enough practice

by Емельянов А


some informations are hard to undferstanding

by إبراهيم ش


Good Course Thanks To All