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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
2,201 個評分
378 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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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.

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.

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126 - Machine Learning: Clustering & Retrieval 的 150 個評論(共 366 個)

創建者 Anshumaan K P

2020年11月11日

Good Specialization. But some assignments make it more cool i.e, not here :)

創建者 Saint-Clair d C L

2016年8月30日

This course has been an amazing experience. Congrats to you, Carlos and Emmy!

創建者 Athanasios K

2021年1月7日

This is an exceptional and challenging specialization. So much to take away

創建者 Ayan M

2016年12月4日

Excellent! Very good material and lectures and hands on. Really enriching.

創建者 Amey B

2016年12月18日

Very Insightful. Great Instructors. Awesome Forum and intelligible peers.

創建者 Muhammad Z H

2019年8月30日

Machine Learning: Clustering & Retrieval, I have learned a lot professor

創建者 YASHKUMAR R T

2019年5月31日

Awesome course to understand the concept behind Gaussian Mixture model.

創建者 Edwin P

2019年2月15日

Excellent, good contribution to the technical and practical knowledge ML

創建者 Parab N S

2019年10月12日

Excellent course on clustering & retrieval by University of Washington

創建者 Manuel A

2019年9月8日

Great course and specialization overall, both lectures and assignments

創建者 Prabhu

2019年11月2日

Very clear explanation of concepts with a good selection of examples.

創建者 Hans H

2018年7月27日

Amazing course, I´ve learned so much stuff that I can use in my job.

創建者 Swapnil A

2020年9月6日

Really awesome course. Dr. Emily explains everything from scratch.

創建者 Jonathan H

2017年7月1日

Emily is great! Excellent course that covers a ton of material!!!

創建者 johny a v o

2020年11月21日

very helpfull the course, congrat!!! and thank u for this course

創建者 Yihong C

2016年9月30日

a practical and interesting course about clustering and retrival

創建者 Ben L

2017年6月10日

The most challenging of the four courses in the specialization.

創建者 Eric N

2020年10月11日

Excellent online teaching with clear and concise explanations!

創建者 Akash G

2019年3月11日

Machine Learning: Clustering & Retrieval good and learn easily

創建者 shaonan

2016年11月20日

Deep insight into most useful techniques of machine learning.

創建者 JOSE R

2017年11月18日

Very well explained. The LDA was difficult to learn. Thanks.

創建者 Daniel R

2016年8月16日

Another great hit by Emily and Carlos!!! Excellent Course!!!

創建者 Yifei L

2016年7月30日

Good course for KD trees, LSH, Gaussian mixed model and LDA.

創建者 Victor C

2017年6月24日

Excellent teacher and material. I wish there were more...

創建者 Francisco R M

2021年3月19日

Too many assingments dedicated to on scratch development.