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Machine Learning: Clustering & Retrieval に戻る

ワシントン大学(University of Washington) による Machine Learning: Clustering & Retrieval の受講者のレビューおよびフィードバック

4.6
1,840件の評価
316件のレビュー

コースについて

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....

人気のレビュー

BK

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.

JM

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.

フィルター:

Machine Learning: Clustering & Retrieval: 251 - 275 / 304 レビュー

by Abhishek S

Feb 10, 2018

Till Expectation Maximization, the learning is tremendous. However, once past that, everything would feel incomplete since most assignments are spoon fed after that. Rating it four stars because of initial lectures.

by Siva J

Feb 26, 2017

Good and deep dive into ML!

Absolutely disappointed that the course was delayed and the promise to take it through Course 5 and Capstone Project didn't come through.

Not at all happy with that!!

by Srinivas C

Jan 07, 2019

This was a really good course, It made me familiar with many tools and techniques used in ML. With this in hand I will be able to go out there and explore and understand things much better.

by Ahmad A

Mar 31, 2017

This course was my first encounter with Machine Learning! The course gave me a good understanding of the different ML algorithms used in clustering and retrieval of data!

by Andrey C

Apr 10, 2017

Overall is great. The LDA and Dendrograms lack quality/specificity and depth of the previous topics. So sad the Specialization collapsed at 4 courses instead of 6.

by Marco A d S M

Oct 20, 2017

As explicações poderiam ser um pouco mais detalhadas neste curto. Tive certa dificuldade em alguns conceitos apresentados, mais do que nos outros cursos.

by Keith D

Jun 19, 2017

I'm disappointed that courses 5 and 6 of the specialization were cancelled. The cancelled capstone was why I purchased this specialization package.

by Manish G

Jan 15, 2020

This topic was very deep and I learnt many complex algos. Would suggest to have some more examples for the algorithms presented in this modules.

by Marcin W

Aug 09, 2016

Very good course. Too long interval between modules make hard for non-Python developers. Easy to forget some of the Python structures.

by Farrukh N A

Mar 17, 2017

Great course on machine learning, however, left us in middle of learning, Recommender System + Deep Learning Capstone is missing

by Iurii S

Nov 26, 2017

Good course overall.

Starting to get more on the side of being mostly implemented and only needing to insert a line or two.

by Ayush K G

Feb 24, 2018

At some topics more explaination (eg. Map reduce and LDA) needed although as a whole it is good course.

by Big O

Dec 21, 2018

More detail on theory behind LDA and HMMs would have been useful. Otherwise, another brilliant course!

by Michael B

Sep 04, 2016

Good survey of the material, but assignments are superficial and don't test thorough understanding.

by Kartoffel

Jul 26, 2016

Great course. Some week were tough others too easy, but general a very interesting course.

by Hristo V

Aug 31, 2016

The last weeks, we went through the material a little bit too fast.

by Andrey T

Aug 11, 2016

I did not understand LDA from the course materials.

by charan S

Jul 30, 2017

Nice intuitive course with lots of understanding.

by Jack B

Mar 04, 2017

Should use pandas instead of Graph Lab Create

by Mehul P

Sep 11, 2017

Nice explanation on clustering methods.

by Adwait B

Jan 26, 2018

Great Course! Tough topics well taught

by ELINGUI P U

Aug 20, 2016

Great course like the others

by Dony A

Jan 05, 2017

awesome clustering course

by Galen S

May 08, 2017

I liked the slides.

by Koen O

Aug 27, 2017

I liked it a lot