Вернуться к Bayesian Methods for Machine Learning

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Оценки: 669

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Рецензии: 197

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...

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.

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автор: Dan

•14 сент. 2018 г.

The topic covered is great but could be improved. I understand that it can be difficult for a foreigner to speak English but that doesn't help to understand the rather technical course. Besides, the formula are given just as is with little intuitive explanation. Example to follow is A. Ng's ML/ AI course which gives a good tradeoff in terms of rigour vs. intuition. Plus I had to purchase some other off line material to better understand "Pattern recognition and Machine Learning" by C. Bishop - which is excellent - to better understand many concepts.

****Generally proper reading material of a couple of pages per lesson should be given. Slides nor audio transcripts, which are less rigorous, are not enough to cover such difficult and technical topics ***

Also the peer review is cumbersome and for me doesn't add value and slows down the certification process. Automatic grading or AI grading would be great !

автор: Adam C

•15 мая 2018 г.

Good attempt, but rough around the edges. The instructions don't cover all of the content in the quizes. There are "tricks" in the quizes and the answers are not-obvious at times, or there are caveats unknown to you. But you get the answers once you fail and read the reasoning. Unfortunately, the notation is a little sloppy and inconsistent at times throughout the lectures. Examples could be completed further. This is a senior undergraduate or graduate level course and without accompanying reading material you have to take a lot of notes through the lecture, pausing the video often. If you're new to this material, the time spent on this course is much greater than the time spent on other Coursera courses due to its high level. I have a PhD in physics, so I have the mathematical capabilities. But I'm relatively new to Bayesian statistics. This course seems to be covering material form Bishop's "Pattern Recognition and Machine Learning" text.

автор: Michael G

•13 мар. 2020 г.

It is a great idea for a course -- very important in today's ML environment.

However, I felt the instructors did not give much of the "big picture" reasons for why they were teaching each individual technical detail. As a result, I know some more math, but not much about how to apply it to ML. I'm going to have to go online and independently read materials available on the subject so I can better internalize this and figure out how to use it for my purposes in ML.

Although I admire the instructors for giving the class in what is obviously not their first language, it was still quite difficult to follow sometimes when words were mumbled or mispronounced. This could be improved if someone technical could review the lecture transcripts and fill in all the errors and [INAUDIBLE] notices.

The programming assignments were OK, but mostly struggling with syntax rather than concepts. The python package GPyOpt that we used has awful documentation, so we were in effect blindly applying some process optimization code to our homework, without any idea of what it was doing to it and how we could adjust the parameters to better suit our particular application.

автор: Karishma D

•25 мар. 2019 г.

Lots of maths! :). Assignments were very interesting as well.

But overall, this has been my favourite course so far. I like how in depth the lectures went into the maths (made me feel like I was back at uni). However, if I did not have a maths + stats background (from university), I think I would have struggled to keep up with the content

Couple of comments though:

1) For the MCMC week, it would have helped my understanding if we had to fit a Bayesian model to a dataset from scratch via our own implementation of Metropolis Hastings for example in addition to using the pymc3 library.

2) For the Gaussian Processes week, it would have helped my understanding if we had to fit a GP to some data via our own implementation in addition to using the GPy library.

автор: Luka N

•10 нояб. 2019 г.

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.

автор: J L

•13 мая 2018 г.

difficult to follow unstructured lecture contents.

автор: Jayaganesh G

•18 нояб. 2017 г.

This course is little difficult. But I could find very helpful.

Also, I didn't find better course on Bayesian anywhere on the net. So I will recommend this if anyone wants to die into bayesian.

автор: Lizbeth R P

•21 янв. 2018 г.

Maths are not easy but not impossible. However I find material not well prepared (defficient mathematical notation). Additionally, it takes a lot of time to get some help from the forums.

I encourage the instructors to revise the provided material.

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автор: Amith P

•28 окт. 2017 г.

doesn't explain many of essential concepts / theories. This course is mainly for those who has graduate or post-graduate level knowledge of statistics, who ironically may not need this course.

автор: Luke B

•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.

автор: Vaibhav O

•3 апр. 2019 г.

Great introduction to Bayesian methods, with quite good hands on assignments. This course will definitely be the first step towards a rigorous study of the field.

автор: Samuel Y

•26 мар. 2018 г.

Many more theoretical formulas and derivations than previous courses of the specialization, which might require quite a bit of probability theory knowledge. But it is really helpful to understand EM and VAE in depth as well as to use GPy/GPyOpt tools in practice. It would be better to have detail explanation for some quizzes.

автор: Peter K

•25 сент. 2018 г.

This course was really good - it started from easy things for beginners and ended with awesome aplication of bayesian neural networks. Since I have masters in Probability and Statistics I was familiar with most of the stuff and I must thank you fot the mathematics and some proofs. It's hard to find such nice math proofs in today's courses, so it is good for non-mathematicians to the science behind these methods.

Most of the lectures were quite good and for beginner who is willing to study many stuff himself it is good. But I must say that some quizes had questions which answers you couldn't find in the lectures. I recommend to add some more reading stuff mainly for beginners.

автор: Ehsan M K

•24 нояб. 2017 г.

In terms of quality of the material, this is one of the best courses I've taken from Coursera! Bear in mind that it is an advanced course and requirements are high. So if your math skills is at graduate student level, you can benefit from this course. The topics are very important and applicable. I really liked all the explicit and detailed calculations done step by step, though I can guess many would find them boring.

However, in terms of TA support, assignments design, it's one of worst courses I've seen in coursera! Instructors or TAs barely respond given few registrations in this release. Assignments miss a lot of things and become increasingly frustrating to work on!

автор: Adrien B

•18 апр. 2020 г.

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

автор: Jean M A S

•3 июня 2018 г.

Well, this course is really good, very demanding, and rigorous. The main disadvantage is the forum. Chances are that nobody will answer your questions, so be prepared to have a raw experience of learning. But if you are serious, you will eventually finish the course, and learn a lot.

автор: Vadim K

•11 сент. 2018 г.

Terrible task design.

No PyMC documentation provided

автор: Mark Z

•4 июня 2019 г.

This course course teaches you a lot of useful math. It might be hard to understand at times, but you will get through it. Assignments are good for getting to know python tools which implement mathematical concepts described in lectures. Overall the best course I've taken so far.

автор: Sun X

•12 июня 2018 г.

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!

автор: Georgi T

•13 апр. 2020 г.

I loved this course. It's just the right difficulty if you have some experience in ML. The exercises teach new frameworks such as PyMC or GPy that can be used in one's future work. Highly recommendable.

автор: Yu Z

•29 мар. 2018 г.

clear instruction and great insights to algorithm, I love it. Really regret for lacking the time to finish all the programming assignments.

автор: Radosław B

•30 дек. 2018 г.

Great mix of theory and practice, without the unnecessary tutorial-like stuff everyone can look up in their search engine of choice.

автор: Zixu Z

•1 дек. 2018 г.

Course content is excellent. However I hope it could have had more about MCMC. That part was pretty thin.

автор: Wei X

•27 авг. 2018 г.

appreciate the balance of introducing the Bayesian statistics and the application of machine learning.

автор: Atul K

•26 нояб. 2017 г.

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

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