Chevron Left
Back to Machine Learning: Clustering & Retrieval

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

2,307 ratings

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


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.


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.

Filter by:

1 - 25 of 381 Reviews for Machine Learning: Clustering & Retrieval

By Ernie M

Sep 25, 2017

By James F

Aug 10, 2016

By Eugene K

Feb 10, 2017

By Veeraraghavan

Mar 2, 2020

By André F d A F C

Jul 25, 2016

By Dario D G

Jan 18, 2020

By Edward F

Jun 25, 2017

By akashkr1498

Jul 8, 2019

By Bruno K

Aug 25, 2016

By Pankaj K

Sep 8, 2017

By Tsz W K

May 14, 2017

By Hamel H

Aug 7, 2016

By Ken C

Feb 4, 2017

By Phil B

Feb 13, 2018

By Sean S

Apr 3, 2018

By Leonardo D

Aug 25, 2019

By Luiz C

Jul 10, 2018

By vacous

Apr 18, 2018

By Kim K L

Oct 4, 2016

By Uday A

Aug 12, 2017

By Diogo A

Jul 17, 2020

By Ridhwanul H

Oct 17, 2017

By Abhilash

Feb 20, 2017

By Swati D

May 2, 2018

By Jie S

Dec 27, 2019