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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

2,307 évaluations

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


16 janv. 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.


24 août 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.

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par Daniel R

16 août 2016

par Yifei L

30 juil. 2016

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par Guillermo O d A

4 juin 2022

par Francisco R M

19 mars 2021

par Moayyad Y

4 déc. 2016

par Fengchen G

2 sept. 2016

par Divyang S

13 sept. 2020

par Yong D K

7 mai 2018

par Sameer M

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par 陈佳艺

17 mai 2017

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par Manoj K

26 nov. 2018

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par Oleg B

3 déc. 2016

par Niu K

3 janv. 2019

par Vladimir V

27 juin 2017

par Kishore P V

5 oct. 2016

par Jaswant J

31 mars 2017

par Yang X

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par Sean L

4 oct. 2016

par Banka C G

10 août 2019

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par Anmol g

16 déc. 2016

par seokwon y

26 juil. 2018