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Learner Reviews & Feedback for Machine Learning: Clustering & Retrieval by University of Washington

4.6
stars
2,201 ratings
378 reviews

About the Course

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....

Top reviews

BK
Aug 24, 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.

JM
Jan 16, 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.

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126 - 150 of 366 Reviews for Machine Learning: Clustering & Retrieval

By Anshumaan K P

Nov 11, 2020

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

By Saint-Clair d C L

Aug 30, 2016

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

By Athanasios K

Jan 7, 2021

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

By Ayan M

Dec 4, 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 12, 2019

Excellent course on clustering & retrieval by University of Washington

By Manuel A

Sep 8, 2019

Great course and specialization overall, both lectures and assignments

By Prabhu

Nov 2, 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 Swapnil A

Sep 6, 2020

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

By Jonathan H

Jul 1, 2017

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

By johny a v o

Nov 21, 2020

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

By Yihong C

Sep 30, 2016

a practical and interesting course about clustering and retrival

By Ben L

Jun 10, 2017

The most challenging of the four courses in the specialization.

By Eric N

Oct 11, 2020

Excellent online teaching with clear and concise explanations!

By Akash G

Mar 11, 2019

Machine Learning: Clustering & Retrieval good and learn easily

By shaonan

Nov 20, 2016

Deep insight into most useful techniques of machine learning.

By JOSE R

Nov 18, 2017

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

By Daniel R

Aug 16, 2016

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

By Yifei L

Jul 30, 2016

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

By Victor C

Jun 24, 2017

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

By Francisco R M

Mar 19, 2021

Too many assingments dedicated to on scratch development.