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Retour à Machine Learning: Clustering & Retrieval

Avis et commentaires pour d'étudiants pour Machine Learning: Clustering & Retrieval par Université de Washington

4.6
stars
1,822 évaluations
313 avis

À propos du cours

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....

Meilleurs avis

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.

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201 - 225 sur 301 Avis pour Machine Learning: Clustering & Retrieval

par Bruno G E

Sep 03, 2016

Simply Amazing!

par Christopher D

Aug 09, 2016

Superb course!

par Jinho L

Sep 20, 2016

Great! thanks

par Hanna L

Sep 02, 2019

Great class!

par Mark h

Aug 08, 2017

Very helpful

par 邓松

Jan 04, 2017

very helpful

par Jiancheng

Oct 27, 2016

Great intro!

par Thuong D H

Sep 23, 2016

Good course!

par Karundeep Y

Sep 18, 2016

Best Course.

par Pradeep N

Feb 22, 2017

"super one,

par clark.bourne

Jan 09, 2017

内容丰富实际,材料全。

par VITTE

Nov 11, 2018

Excellent.

par Gautam.R

Oct 08, 2016

Awesome :)

par RISHABH T

Nov 12, 2017

excellent

par Iñigo C S

Aug 08, 2016

Amazing.

par Bingyan C

Dec 27, 2016

great.

par Cuiqing L

Nov 05, 2016

great!

par Job W

Jul 23, 2016

Great!

par Frank

Nov 23, 2016

非常棒!

par Alexandre

Oct 23, 2016

ok

par Nagendra K M R

Nov 11, 2018

G

par Suneel M

May 09, 2018

E

par Lalithmohan S

Mar 26, 2018

V

par Ruchi S

Jan 24, 2018

E

par Kevin C N

Mar 26, 2017

E