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Kursteilnehmer-Bewertung und -Feedback für Machine Learning: Clustering & Retrieval von University of Washington

1,968 Bewertungen
335 Bewertungen

Über den Kurs

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....



Jan 17, 2017

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.


Aug 25, 2016

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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126 - 150 von 323 Bewertungen für Machine Learning: Clustering & Retrieval

von Prabhu

Nov 02, 2019

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

von Hans H

Jul 27, 2018

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

von Jonathan H

Jul 01, 2017

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

von Yihong C

Sep 30, 2016

a practical and interesting course about clustering and retrival

von Ben L

Jun 11, 2017

The most challenging of the four courses in the specialization.

von Akash G

Mar 11, 2019

Machine Learning: Clustering & Retrieval good and learn easily

von shaonan

Nov 20, 2016

Deep insight into most useful techniques of machine learning.

von JOSE R

Nov 18, 2017

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

von Daniel R

Aug 17, 2016

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

von Yifei L

Jul 30, 2016

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

von Victor C

Jun 24, 2017

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

von Moayyad A Y

Dec 04, 2016

this is not a an easy course but certainly an awesome one

von Fengchen G

Sep 02, 2016

Awesome course! The session on EM algorithm is revealing!

von Yong D K

May 07, 2018

This is the best course for Information Retrieval ever!

von Sameer M

Sep 19, 2017

Excellent course! must for machine learning beginners!!

von 陈佳艺

May 17, 2017

sometimes difficult,but import so many useful knowledge

von 백원광

Jan 17, 2017

Very sophisticated, friendly and practical instructions

von Manoj K

Nov 26, 2018

session was very helpful & full with relevant contents

von Siwei Y

Jan 17, 2017

本来不报什么期望,但是该门课确实做得相当好。 相信该课的老师们花了巨大的心血。真的是业界良心。所以强烈点赞。

von Oleg B

Dec 04, 2016

Great course, very hands-on, very practical knowledge.

von KAI N

Jan 03, 2019

Excellent course with great and reachable explanation

von Vladimir V

Jun 27, 2017

Awesome course. Thank you Emily, Carlos and Coursera!

von Kishore P V

Oct 05, 2016

One of the best machine learning course I have taken.

von Jaswant J

Mar 31, 2017

Very nice course. Concepts are covered very clearly.

von Yang X

Nov 15, 2017

Thank you Emily and Carlos! You guys are amazing!!!