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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.


76 - Machine Learning: Clustering & Retrieval 的 100 個評論(共 296 個)

創建者 Robi s

Sep 18, 2017

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

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 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!


創建者 João F A d S

Aug 07, 2016

Great course. Well packed, well explained, nice practical examples, good all around MOOC with of info.

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 Snehotosh K B

Dec 03, 2016

Best course available till date as MooC

創建者 krishna k s

Apr 20, 2018

This is very nice and interesting course. It gives practical application of machine learning application. I would consider this course as applied machine learning course as it lacks mathematical intuition. Nevertheless, course it great and cover major points in the machine learning field.

創建者 Sumit

Sep 17, 2016

Excellent course

創建者 Miao J

Jul 01, 2016

Another great course. Strongly recommend!

創建者 Alfred D

Mar 24, 2018

KD trees, LSH along with LDH were some real deep techniques I've learnt and benefitted.

Thanks a ton to Emily and Carlos , you guys are amazing teachers for such a complex subject as ML and the algorithms it consists of .

創建者 Saqib N S

Dec 05, 2016

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

創建者 Justin K

Aug 17, 2016

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

創建者 Russell H

Oct 09, 2016

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

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 Bruno C K

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.

創建者 Gautam.R

Oct 08, 2016

Awesome :)

創建者 Robert C

Feb 16, 2018

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