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學生對 华盛顿大学 提供的 Machine Learning: Clustering & Retrieval 的評價和反饋

4.6
2,163 個評分
371 條評論

課程概述

Case Studies: Finding Similar Documents A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each time you want to a retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover? In this third case study, finding similar documents, you will examine similarity-based algorithms for retrieval. In this course, you will also examine structured representations for describing the documents in the corpus, including clustering and mixed membership models, such as latent Dirichlet allocation (LDA). You will implement expectation maximization (EM) to learn the document clusterings, and see how to scale the methods using MapReduce. Learning Outcomes: By the end of this course, you will be able to: -Create a document retrieval system using k-nearest neighbors. -Identify various similarity metrics for text data. -Reduce computations in k-nearest neighbor search by using KD-trees. -Produce approximate nearest neighbors using locality sensitive hashing. -Compare and contrast supervised and unsupervised learning tasks. -Cluster documents by topic using k-means. -Describe how to parallelize k-means using MapReduce. -Examine probabilistic clustering approaches using mixtures models. -Fit a mixture of Gaussian model using expectation maximization (EM). -Perform mixed membership modeling using latent Dirichlet allocation (LDA). -Describe the steps of a Gibbs sampler and how to use its output to draw inferences. -Compare and contrast initialization techniques for non-convex optimization objectives. -Implement these techniques in Python....

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JM
2017年1月16日

Excellent course, well thought out lectures and problem sets. The programming assignments offer an appropriate amount of guidance that allows the students to work through the material on their own.

BK
2016年8月24日

excellent material! It would be nice, however, to mention some reading material, books or articles, for those interested in the details and the theories behind the concepts presented in the course.

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176 - Machine Learning: Clustering & Retrieval 的 200 個評論(共 361 個)

創建者 Kan C Y

2017年3月19日

Really a good course, succinct and concise.

創建者 parag_verma

2020年1月7日

Thanks to the entire team of this course.

創建者 PRAVEEN R U

2018年12月27日

Nice content and well made presentations.

創建者 Miao J

2016年7月1日

Another great course. Strongly recommend!

創建者 Veer A S

2018年3月23日

Very informative and interesting course.

創建者 Ted T

2017年7月29日

Best ML course ever. Easy to understand!

創建者 Dmitri T

2016年12月4日

Great course! Very simple and practical.

創建者 Veera K R

2020年4月6日

Very informative and Clearly explained.

創建者 Snehotosh B

2016年12月3日

Best course available till date as MooC

創建者 kripa s

2019年4月30日

One of the best training experience...

創建者 Shuang D

2018年6月29日

advanced knowledge on ML, great course

創建者 Garvish

2017年6月14日

Great Information and organised course

創建者 RAJIT N

2020年9月21日

Everything was very clearly explained

創建者 Ce J

2017年6月26日

well organized and easy to understand

創建者 李紹弘

2017年8月22日

This course provides concise course.

創建者 Nada M

2017年6月11日

Thank you! I loved all your classes.

創建者 Fernando B

2017年2月21日

Best Course on ML yet on the Web

創建者 AANCHAL k

2020年10月17日

It was brelient , just no words

創建者 Matheus F

2018年8月10日

Excelent course! Very helpful!

創建者 Foo C S G

2018年3月4日

Tough slog, but well designed

創建者 Roger S

2016年9月4日

Worth the wait. COOL learning

創建者 Danylo D

2016年12月6日

Thank you, it was a good one

創建者 Sandeep J

2016年9月4日

Best course I've taken!! :)

創建者 Alessandro B

2017年12月15日

very useful and structured

創建者 wonjai c

2020年5月19日

difficult but good enough