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

4.7
stars
2,343 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

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.

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.

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76 - 100 of 387 Reviews for Machine Learning: Clustering & Retrieval

By Brandon H

Dec 14, 2016

This was probably the most challenging course of them all, I thoroughly enjoyed it! Looking forward to dimensionality reduction and the capstone.

By Tripat S

Aug 7, 2016

This is the best course in ML - would recommend it ...the sequence of the courses is the best...the specialization in this ML is a career boost

By sandeep d

Aug 20, 2020

excellent course by Emily and Carlos

I am glad to have this course

it contains clear view regarding clustering and its applications from roots

By Shaowei P

Aug 8, 2016

very good course but the last few topics could be improved with better assignments that could be broken down into smaller sub assignments

By Jared C

Aug 7, 2016

Exceptional course! This is challenging material for me, but it's presented in such a coherent manner that you can't help but absorb it.

By Saqib N S

Dec 4, 2016

The course dived into basic and advanced concepts of unsupervised learning. As before, Prof Fox did a great job at explaining things.

By Yao X

Sep 29, 2019

Wish to have more detail on implementing the algorithm. Assignments are too easy for understanding the knowledge behind the scene.

By Songxiang L

Dec 4, 2016

Very good, not only learn many good ML concepts, but also polish my python programming skill a lot. Thank you, Emily and Carlos.

By Dongliang Z

Mar 22, 2018

I enjoyed this course. This specialization is very good for machine learning beginner. Look forward to the next course anyway.

By Целых А Н

Jun 7, 2020

Find the course useful. The authors presented a simple and clear visualization of the meaning of algorithms. Excellent!

Thanks!

By Robert C

Feb 16, 2018

Emily was fantastic at explaining difficult to understand concepts. Thoroughly enjoyed the course, and learned quite a lot.

By Kuntal G

Nov 3, 2016

Very Good in depth explanation and hand-on lab machine learning course. very focused on real world analytics and algorithms

By Arun K P

Oct 27, 2018

Very useful and informative .It help and provide confidence to the job more effectively. Thanks for the help and good cour

By Jose J M T

Apr 14, 2017

The teachers are really amazing. They do not just explain it as if they read a book. They explain the concepts very well

By Vikash S N

Feb 3, 2019

It was great but I was also interested to implement the solutions with pyspark...though I did it eventually. Thank you!

By Marc G

Oct 21, 2017

Clear and well designed course. The assignments are quite thorough. Sometimes, quiz question are not so clear though.

By Andrey N

Mar 12, 2017

Some themes are shown very superficially it would be great to go deeper. Despite of this the course is great!

Thanks.

By Rohan K

Mar 22, 2018

Good introduction to very complicated concepts. I now have the tools to learn more about HHMs and anomaly detection.

By Justin K

Aug 17, 2016

An interesting topic, presented well by the instructor and reinforced by intermediate-level programming assignments.

By Somu P

Nov 17, 2018

Excellent course, which gives you all you need to learn about machine learning. Concepts and hands on practical ex

By Édney M V F

Jan 10, 2022

Diferente dos demais cursos esse é muito mais direto, depende diretamente de você ter feito os cursos anteriores.

By Freeze F

Oct 26, 2016

From LDA onwards the pace ramped up ! Please be slow during advance topics. But altogether it was a great course.

By Fahad S

Nov 3, 2018

Emily ross is an amazing instructor. The course introduces many complex topics and presents them intuitively.

By Patrick M

Aug 8, 2016

Excellent course. Nice selection of algorithms reviewed - all clearly explained with sample implementations.

By Jorge L

May 26, 2017

I'm a grad student and I can notice the instructor makes a difference in this course. I fully recommend it.