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學生對 华盛顿大学 提供的 Machine Learning: Clustering & Retrieval 的評價和反饋

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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 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.


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.


26 - Machine Learning: Clustering & Retrieval 的 50 個評論(共 295 個)

創建者 Manuel S

Oct 01, 2016

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

創建者 Liling T

Aug 15, 2016

Emily Fox did a great job in explaining tough concepts with simple explanation of the components in the formulas!!

It's a little tough to get through the materials though, it's the 4th course in University of Washington's machine learning specialization afterall =)

創建者 Moises V

Oct 30, 2016

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

創建者 Oleg B

Dec 04, 2016

Great course, very hands-on, very practical knowledge.

創建者 Chandrashekar T

Oct 11, 2016

The material covered in this course is immense and gives a deep understanding of several algorithms required to perform unsupervised learning tasks.

創建者 Mostafa A M

Aug 28, 2016

Fantastic course as usual

創建者 Antonio P L

Oct 03, 2016

Excellent course.

創建者 Thuong D H

Sep 23, 2016

Good course!

創建者 Miguel P

Jul 13, 2016

I loved the previous 3 courses and what I saw in this course so far seems pretty interesting. I'm really sad that Coursera decided to block access to assignments for not paying users. I really wanted to continue with the specialization but I already purchased another specialization, so I'm going to have to put this course on hold for a now.

創建者 Vladimir V

Jun 27, 2017

Awesome course. Thank you Emily, Carlos and Coursera!

創建者 Venkateshwaralu

Aug 07, 2016

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

創建者 Arash A

Jan 05, 2017

Enjoyed the course and learned a lot. Amazing!

創建者 Kishore P V

Oct 05, 2016

One of the best machine learning course I have taken.

創建者 Sandeep J

Sep 04, 2016

Best course I've taken!! :)

創建者 Michael B

Jul 12, 2016

Not for the faint of heart but this course does a really good job of explaining clustering (and retrieval) of images and text. It includes several programming assignments which can be tackled with minimal programming experience if one perseveres.

創建者 Rama K R N R G

Sep 09, 2017

Good presentation of topics. Detailed walk through of few advanced topics covered at the end would have been great. Felt the presentation went too fast.

創建者 Renato R S

Aug 27, 2016

A perfect and balanced introduction to the subjects, adding theory and practice beautifully.

創建者 Omar S

Jul 12, 2017

I loved this course!

創建者 Fengchen G

Sep 02, 2016

Awesome course! The session on EM algorithm is revealing!

創建者 Jared C

Aug 07, 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.

創建者 Bruno G E

Sep 03, 2016

Simply Amazing!

創建者 Ted T

Jul 29, 2017

Best ML course ever. Easy to understand!

創建者 Gautam.R

Oct 08, 2016

Awesome :)

創建者 clark.bourne

Jan 09, 2017


創建者 Muhammad H A

Aug 13, 2016

I used to run into a wall at work trying to train models with recursive partitioning or neural nets because of the long time they took to train for high dimensional data. These clustering techniques are an immense help.

Awesome course, with a brilliant instructor and brilliant assignments.