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Machine Learning: Clustering & Retrieval に戻る

ワシントン大学(University of Washington) による Machine Learning: Clustering & Retrieval の受講者のレビューおよびフィードバック

4.6
1,848件の評価
317件のレビュー

コースについて

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....

人気のレビュー

JM

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.

BK

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.

フィルター:

Machine Learning: Clustering & Retrieval: 101 - 125 / 306 レビュー

by Yugandhar D

Oct 29, 2018

Excellent course on clustering and retreival. The assignments were thorough and productive.

by Sathiraju E

Mar 03, 2019

Very nice course. Things are well explained, however some concepts could be expanded more.

by Moises V

Oct 30, 2016

I loved this course. then content is designed to acquire strong foundations in clustering.

by Yi W

Sep 28, 2016

As someone very keen on math, more math background as optimal video would be more helpful.

by austin

Aug 09, 2017

Awesome course. Very detailed and thorough, and the bonus sections are really useful too.

by Venkateshwaralu

Aug 07, 2016

Sets a new benchmark for the specialization !!! A great offering on Machine Learning :)

by Jifu Z

Jul 23, 2016

Good class, But it would be much better if the quiz is open to those who doesn't pay.

by Robi s

Sep 18, 2017

Great instruction, great course, and provide information I used directly in my work.

by Russell H

Oct 09, 2016

Detailed coverage of several approaches to clustering. Not easy but learned a lot.

by Manuel S

Oct 01, 2016

Amazing course, really helpful, as a ML researcher you need this kind of foundation

by Shuyi C

Aug 19, 2019

I think it is easy to understand and good to practice. Nice entry level course!

by Saint-Clair d C L

Aug 30, 2016

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

by Ayan M

Dec 04, 2016

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

by Amey B

Dec 18, 2016

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

by Muhammad Z H

Aug 30, 2019

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

by YASHKUMAR R T

May 31, 2019

Awesome course to understand the concept behind Gaussian Mixture model.

by Edwin P

Feb 15, 2019

Excellent, good contribution to the technical and practical knowledge ML

by Parab N S

Oct 13, 2019

Excellent course on clustering & retrieval by University of Washington

by Manuel A

Sep 08, 2019

Great course and specialization overall, both lectures and assignments

by Prabhu

Nov 02, 2019

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

by Hans H

Jul 27, 2018

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

by Jonathan H

Jul 01, 2017

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

by Yihong C

Sep 30, 2016

a practical and interesting course about clustering and retrival

by Ben L

Jun 11, 2017

The most challenging of the four courses in the specialization.

by Akash G

Mar 11, 2019

Machine Learning: Clustering & Retrieval good and learn easily