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Отзывы учащихся о курсе Bayesian Methods for Machine Learning от партнера НИУ ВШЭ

4.5
звезд
Оценки: 685
Рецензии: 202

О курсе

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: coursera@hse.ru...

Лучшие рецензии

JG
17 нояб. 2017 г.

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
6 июня 2019 г.

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.

Фильтр по:

26–50 из 196 отзывов о курсе Bayesian Methods for Machine Learning

автор: Atul K

26 нояб. 2017 г.

Excellent content, we need more advanced courses like this. Assignments are also very interesting.

автор: Tatyana P

1 апр. 2020 г.

Very thorough and rigorous course. Whiteboard (or transparent board) derivations were priceless.

автор: Yanting H

18 сент. 2018 г.

A very detailed course for someone who wants to strengthen their statistical background.

автор: Голубев К О

19 окт. 2018 г.

Great course with fine lecturers and deep immersion in Bayesian methods

автор: Dongxiao Z

11 окт. 2018 г.

Learned a lot from this course. Thanks!

автор: Alek R

12 нояб. 2018 г.

super helpful and very applicable!

автор: Anmol g

6 дек. 2018 г.

One of the best in-depth course.

автор: sagar s

29 сент. 2018 г.

Awesome. Worth it!

автор: Max P Z

2 апр. 2018 г.

Tough but useful!

автор: Ertan T

25 апр. 2018 г.

Superb Course

автор: Milo V

19 июня 2018 г.

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.

автор: Daniel T

6 авг. 2019 г.

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.

автор: Maciej

24 мар. 2019 г.

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.

автор: Pengchong L

27 авг. 2018 г.

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.

автор: Artem E

3 июня 2018 г.

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.

автор: Aviv B

18 мар. 2020 г.

Explanations are very technical and do not develop any intuition as to what the methods are supposed to accomplish.

автор: 张学立

8 нояб. 2017 г.

it seems that the prof didn't prepare the course well

автор: Dizhao J

8 авг. 2018 г.

very bad Interpretation

автор: Molaee A B

7 мая 2021 г.

A good course for those who already have a good knowledge on Bayesian methods and want to deepen their knowledge.

The course is a bit difficult and you need to take notes, and it takes a bit of time to finish it.

Practical python exercises help you to better understand the course, BUT they should not have been introduced as exams.

The instructors should have provided some courses about the contents of the python exercises.

Without providing this information many students cannot solve the python exercises and they should find the answers in internet !!!

But in general, I recommend the course, and I would like to thank instructors.

автор: Novin S

3 февр. 2020 г.

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.

автор: Mayukh S

11 мая 2020 г.

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.

автор: Martin K

16 мар. 2018 г.

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!

автор: Jordi W

28 февр. 2019 г.

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.

автор: Erwin P

17 мар. 2019 г.

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.

автор: Marcos C

17 окт. 2019 г.

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.