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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
étoiles
2,183 évaluations
375 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
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.

JM
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.

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226 - 250 sur 363 Avis pour Machine Learning: Clustering & Retrieval

par Xue

18 déc. 2018

Great but hard~!

par 嵇昊雨

25 avr. 2017

内容深度可以,对个人的帮助比较大

par Daniel W

23 déc. 2016

Excellent course

par Sumit

16 sept. 2016

Excellent course

par Phan T B

8 août 2016

very good course

par Md. K H T

25 juil. 2020

Awesome Course.

par IDOWU H A

20 mai 2018

Excellent - Goo

par vivek k

24 mai 2017

awesome course!

par Bruno G E

3 sept. 2016

Simply Amazing!

par Christopher D

9 août 2016

Superb course!

par Jinho L

19 sept. 2016

Great! thanks

par Pakomius Y N

28 sept. 2020

Terima Kasih

par Divyanshu S

27 août 2020

Very helpful

par JOYDIP M

30 juil. 2020

very helpful

par Manikant R

20 juin 2020

Great course

par ANKUR S

14 avr. 2020

loved it..!!

par Hanna L

1 sept. 2019

Great class!

par Mark h

8 août 2017

Very helpful

par 邓松

4 janv. 2017

very helpful

par Jiancheng

26 oct. 2016

Great intro!

par Thuong D H

22 sept. 2016

Good course!

par Karundeep Y

18 sept. 2016

Best Course.

par Siddharth V B

29 nov. 2020

nice course

par Saurabh A

24 sept. 2020

very good !

par Pradeep N

21 févr. 2017

"super one,