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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
638 個評分
186 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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51 - Bayesian Methods for Machine Learning 的 75 個評論(共 180 個)

創建者 Bob F

2018年3月11日

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!

創建者 Ayush T

2019年8月24日

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.

創建者 Pablo V I

2020年1月13日

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.

創建者 Kuldeep J

2019年4月3日

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.

創建者 Igor B

2019年4月18日

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.

創建者 Daniel B

2020年12月14日

I really like this course! The selection of course material and assignments was interesting, motivating, and challenging. They didn't shy away from confronting their students with the necessary math to foster a deep understanding of the material which I appreciate a lot. Great job!

創建者 Thomas F

2020年1月11日

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!

創建者 Hythem S

2017年12月16日

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.

創建者 Peppe G

2018年2月20日

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.

創建者 Debasish G

2019年11月14日

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.

創建者 Diogo P

2018年1月5日

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.

創建者 Senthilmurugan S

2020年6月23日

I really loved the content of this course. It has a very good mix of strong theory and practical assignments. Lecture contents are of very high quality.

Key point: Course contents are not diluted to reach the masses; Equivalent of class-room course

創建者 Sasanko S G

2020年9月29日

Excellent course design with a moderated degree of difficulties in quizzes, assignments, and projects. The theoretical explanation of the basic to advanced analysis is wonderful and very much motivational to go with advanced Bayesian concepts.

創建者 Abhishek S

2020年9月8日

HSE courses are really good, they have made esoteric concepts very easy to understand. The course provides intuitions of algorithms. Highly recommended to have a good understanding of the probabilistic understanding of machine learning.

創建者 SRINJOY G

2020年3月2日

Truly amazing course and the only course online which covers various essential aspects of Bayesian Machine Learning and Bayesian Deep Learning which is at the forefront of the research today in the field of AI and Machine Learning.

創建者 Chan H Y

2018年5月8日

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

創建者 Guillermo P T

2020年6月8日

Great course! Very advanced concepts are shown through the lectures, and the lecturers have a huge knowledge on the field. I've learned a lot, and the concepts learnt through these weeks will profit me in my professional career.

創建者 Fabian C G R

2020年9月24日

It is a very good course, it gives the theoretical elements that many other courses take for granted and it does it in a very pleasant way to understand. The only thing I don't like is the peer review of the last assignment.

創建者 David G

2018年8月21日

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.

創建者 Hyunseok

2020年5月10日

Nice lecture!

Two lecturers explained the meaning of mathematics process in the Bayesian methods. It was very helpful.

Also, I got useful tools in machine learning problem by using helpful library!

創建者 Ashish D

2020年11月1日

A bit more background on the maths used would go a long way n better elucidating the concepts. All in all a great course with a suitable level of detail, Kudos!

創建者 Liu Y

2018年3月16日

Concise but very informative, challenges not only from knowledge but also from various tools if you've never met them before. Indeed, great course!

創建者 Tomaso V

2020年5月15日

The topics are very interesting, and clearly explained.

I really appreciated the questions included in the lessons, which help to keep attention.

創建者 Alex E

2018年5月9日

Challenging, but well designed course covering cutting edge ML methods. The course assumes high proficency with Tensorflow, Keras, and Python.

創建者 Yu C

2020年8月26日

It delivers the rigor and the insights. Would be even better if the assignments leaves implementation of some algorithms to be done manually.