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學生對 俄罗斯国家研究型高等经济大学 提供的 Addressing Large Hadron Collider Challenges by Machine Learning 的評價和反饋

4.5
103 個評分
16 條評論

課程概述

The Large Hadron Collider (LHC) is the largest data generation machine for the time being. It doesn’t produce the big data, the data is gigantic. Just one of the four experiments generates thousands gigabytes per second. The intensity of data flow is only going to be increased over the time. So the data processing techniques have to be quite sophisticated and unique. In this online course we’ll introduce students into the main concepts of the Physics behind those data flow so the main puzzles of the Universe Physicists are seeking answers for will be much more transparent. Of course we will scrutinize the major stages of the data processing pipelines, and focus on the role of the Machine Learning techniques for such tasks as track pattern recognition, particle identification, online real-time processing (triggers) and search for very rare decays. The assignments of this course will give you opportunity to apply your skills in the search for the New Physics using advanced data analysis techniques. Upon the completion of the course you will understand both the principles of the Experimental Physics and Machine Learning much better. Do you have technical problems? Write to us: coursera@hse.ru...

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1 - Addressing Large Hadron Collider Challenges by Machine Learning 的 15 個評論(共 15 個)

創建者 Shanaya M

2018年8月30日

For an undergrad student of computer science, this course provides great insights into the world of astrophysics and how machine learning can be applied to solve some of the greatest mysteries of the universe.

創建者 Wei X

2018年10月17日

nice starting point for graduate students or senior undergraduate students who want to dig deeper in this direction

創建者 Vaibhav O

2019年4月24日

Some assignments are too abstract and difficult to get through without external help

創建者 Milos V

2019年3月8日

This course was walk in the park in comparison to the other ones in the specialization. However, it would not be so if I did not complete all of the previous ones. Non-perfect score goes because I think that practical assignments should be better explained like: "do some feature engineering", "feel free to use any models", etc.

創建者 James h

2019年1月14日

FUN !!!!

創建者 Samuel Y

2020年3月25日

The course material is quite brief and introductive for particle physics, with only a few interesting machine learning tricks. Meanwhile, the assignments are less prepared even misguided, either need blindly tuning sklearn optimizer or heavily dependent on feature engineering, which are not related to the core knowledge of the session. From my point of view, this course is not as good as the other ones in this great machine learning specialization.

創建者 Mohammed F

2019年12月30日

A challenging ML course for practitioners and researchers to put their abilities to the test. Could have enjoyed a bit more (possibly optional) explanation about the underlying physics.

創建者 DUSHYANT A

2020年7月24日

Awesome Course! Puts everything in perspective with real world and real experimental data. Those who understand the real value of the data and understand the importance of the data would certainly enjoy this course. Thank you for organizing this last course in the specialization.

創建者 Mario A d l T C

2020年9月28日

Vary hard, and very exciting, But I wish some lectures about the algorithms itself. And reviews about the work in reality vs the slices of the datasets.

創建者 Michael D

2021年1月4日

Very nice overview!

Especially for an ex-experimental particle physicist being out of that business for more than 20 years.

創建者 Hidemasa O

2021年3月27日

it's really exciting! just try it! it's fun!

創建者 4NM16EC026 B S K

2020年9月16日

This course is really good...

創建者 MD A R A

2020年9月6日

Excellent !!!

創建者 Krishna H

2020年8月28日

Great!

創建者 Dupont C

2021年4月24日

Cours extrêmement intéressant mais notion parfois poussée pour des novices; il confirme les acquis de machine learning en trouvant une application physique d'actualité qu'est la détection de particule élémentaire dans le modèle standard.