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學生對 国立高等经济大学 提供的 Bayesian Methods for Machine Learning 的評價和反饋

4.5
620 個評分
182 條評論

課程概述

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 six weeks 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 be found with Bayesian methods. Do you have technical problems? Write to us: coursera@hse.ru...

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JG
2017年11月17日

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.

LB
2019年6月6日

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.

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126 - Bayesian Methods for Machine Learning 的 150 個評論(共 176 個)

創建者 Goh

2019年7月4日

Excellent!

創建者 Sankarshan M

2019年7月9日

very good

創建者 Amrith S

2018年5月17日

Succinct!

創建者 Kelvin L

2018年5月25日

Cool!!

創建者 Hrushikesh L

2020年7月2日

gOOD

創建者 Dipanjan D

2020年1月21日

.

創建者 Marcin L

2019年11月14日

The course was great, I've learned a lot about Bayesian perspective on Machine Learning. The level was satisfying, tasks and quizzes were demanding. It has been very interesting material to learn about.

I would give 5 stars, but eventually gave 4 because it had two drawbacks. First is, assignments are written in tensorflow v1, and occasionally there were issues with compatibility in some libraries. I don't know when the codes were last time refreshed, but unfortunately open source technologies tend to become deprecated very quickly, and the time has already affected course materials. Secondly, some of the most important derivations were made on blackboard, and are not included in downloadable slides. I would really like to keep them in some files, but the're not available.

Apart from these minor drawbacks, it's still a great course and definitely worth learning from.

創建者 Erik B

2020年7月22日

The material that was discussed was quite interesting. In particular, the variational inference, varfational auto encoders, and Gaussion process optimization.

I found the course material a bit lacking tough. There was one lecture where the main formulas for Guassian inference were derived that had a huge mistake in it. Also, sometimes, the assignments were totally unclear. The final project was a bit demotivating. Given the low quality of the pre-trained VAE, the results are not that good. If a pre-trained VAE was used, then why not simply use a bigger pre-trained VAE? Also, the final assignment was really easy and the whole peer review process felt a bit over the top. I must have spent more time in the forums getting my work reviewed than working on the final project.

創建者 Mehrdad S

2019年9月3日

This is a great course for some of the advance topics in Baysian ML. The course starts off great and provides great explanation of the basic topics such as Conjugate, EM algorithm, etc. The related HW are also intelligently designed and fun to solve. But, as it reaches the weeks 5 and 6, things starts to fall apart and the materials are not presented and explained in the best possible way. I think the instructors try to teach many topics which requires a little bit of patience in a short amount of time. Overall, I believe its a course worthy of try, certainly provides great exposure to some of the advance topics but requires further follow ups and studies to completely digest all the materials.

創建者 Raffael S

2020年5月15日

This course is very good. However, the weeks on Variational Methods and Gaussian Processes need more detail or references to extra reading material as they don't very much into depth. Also, a few theoretical exercises would have been nice. E.g. calculating a simple example with non-conjugate priors. Finally, I feel like the notebooks could do with a major update to TF 2.0 and Keras as well as GPy. I spend a few hours chasing non-existent bugs in my code when the problem was that the solutions changed numerically from one version to the other and you have to find out which one.

創建者 Bart-Jan V

2018年11月23日

Great course, great material, though difficult to follow a non native English speaker being non-english myself. Though the instructors know what they are talking about, they don't tell it in their own words but rather seem to have practiced their text.

Another important point is that it took me a lot of time to follow (pre)calculus and probability theory courses, to be able to understand this course. The course was a nice motivation to do that. I'm glad I did, because now I can understand and use VAE's and bayesian optimization (and some other useful stuff)

創建者 Joris D

2018年7月17日

I can not recommend this course highly enough. Unfortunately I can't give it 5 stars since some of the computer assignments were outdated with respect to the tools they utilize (e.g. arguments in the assignments not existing anymore). Still, let that not discourage you. If you ever mentally disconnect when people start talking about Gibbs sampling, mean field approximations, intractable variational lower bounds, or other big fancy words, this is definitely the course for you. You'll discover that all these things are actually quite straightforward.

創建者 Pallavi J

2020年6月11日

This course covered everything I wanted to learn about Bayesian approaches to machine learning. Also, quizes are informative as in if I select something wrong, the valuable feedback is given as in why this option should not be selected which clearly shows that course creators just do not want to make the quizes complex but also want students to learn through those quizes.

Thanks for the course!

創建者 Saptashwa B

2020年3月4日

Fantastic course! Very comprehensive introduction to Bayesian analysis. There's though room for improvement from what I have experienced. One suggestion I have is to provide transcript written and checked by the lecturers and not some auto generated script! This would help us a lot better to understand the video and won't mislead us, which at times the transcript really does!!

創建者 Maury S

2018年8月22日

Excellent, detailed content for people wanting to understand variational methods for machine learning. Fairly high degree of math and statistics required as a prerequisite, as well as moderate ability as a Python programmer. Does not get 5 stars because some of the assignments had confusing instructions, and availability of instructors and others to asnwer questions was poor.

創建者 Cristian M B B

2020年7月19日

This course requires some prior knowledge in probability and statistics to full understand the topics, otherwise is imposible. It's very challenging but useful and interesting. Assignments are well designed for the platform but estimated time to complete them is a bit vague. Learner espent too much time solving this assignments. Otherwise it's an excellent course.

創建者 Mauro D S

2018年7月3日

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?)!

創建者 Milos V

2019年1月8日

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.

創建者 Alexander E

2018年6月2日

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).

創建者 魏力

2018年7月21日

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.

創建者 Gavin L

2020年5月11日

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.

創建者 冯迪(Feng D

2018年2月26日

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.

創建者 Ankit Y

2020年9月29日

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.

創建者 Крюков Д О

2020年9月24日

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.

創建者 Ishaan B

2018年11月28日

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.