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学生对 华盛顿大学 提供的 Machine Learning: Clustering & Retrieval 的评价和反馈

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390 条评论


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....



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.


126 - Machine Learning: Clustering & Retrieval 的 150 个评论(共 378 个)

创建者 Robi s

Sep 17, 2017

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

创建者 Russell H

Oct 9, 2016

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

创建者 Manuel S

Oct 1, 2016

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

创建者 Shuyi C

Aug 19, 2019

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

创建者 Anshumaan K P

Nov 11, 2020

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

创建者 Saint-Clair d C L

Aug 30, 2016

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

创建者 Thanos K

Jan 7, 2021

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

创建者 Ayan M

Dec 4, 2016

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

创建者 kamez 0

Dec 18, 2016

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

创建者 Muhammad Z H

Aug 30, 2019

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


May 31, 2019

Awesome course to understand the concept behind Gaussian Mixture model.

创建者 Edwin P

Feb 15, 2019

Excellent, good contribution to the technical and practical knowledge ML

创建者 Parab N S

Oct 12, 2019

Excellent course on clustering & retrieval by University of Washington

创建者 Manuel A

Sep 8, 2019

Great course and specialization overall, both lectures and assignments

创建者 Prabhu D

Nov 2, 2019

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

创建者 Hans H

Jul 27, 2018

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

创建者 Swapnil A

Sep 6, 2020

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

创建者 Jonathan H

Jul 1, 2017

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

创建者 johny a v o

Nov 21, 2020

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

创建者 Yihong C

Sep 30, 2016

a practical and interesting course about clustering and retrival

创建者 Ben L

Jun 10, 2017

The most challenging of the four courses in the specialization.

创建者 Eric N

Oct 11, 2020

Excellent online teaching with clear and concise explanations!

创建者 Akash G

Mar 11, 2019

Machine Learning: Clustering & Retrieval good and learn easily

创建者 Shaonan W

Nov 20, 2016

Deep insight into most useful techniques of machine learning.

创建者 JOSE R

Nov 18, 2017

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