Chevron Left
Retour à Machine Learning: Clustering & Retrieval

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

4.6
étoiles
2,301 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

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.

Filtrer par :

126 - 150 sur 381 Avis pour Machine Learning: Clustering & Retrieval

par Robi s

•

17 sept. 2017

par Russell H

•

9 oct. 2016

par Manuel S

•

1 oct. 2016

par Shuyi C

•

19 août 2019

par Anshumaan K P

•

11 nov. 2020

par Saint-Clair d C L

•

30 août 2016

par Thanos K

•

7 janv. 2021

par Ayan M

•

4 déc. 2016

par kamez 0

•

18 déc. 2016

par Muhammad Z H

•

30 août 2019

par YASHKUMAR R T

•

31 mai 2019

par Edwin P

•

15 févr. 2019

par Parab N S

•

12 oct. 2019

par Manuel A

•

8 sept. 2019

par Prabhu D

•

2 nov. 2019

par Hans H

•

27 juil. 2018

par Swapnil A

•

6 sept. 2020

par Jonathan H

•

1 juil. 2017

par johny a v o

•

21 nov. 2020

par Yihong C

•

30 sept. 2016

par Ben L

•

10 juin 2017

par Eric N

•

11 oct. 2020

par Akash G

•

11 mars 2019

par Shaonan W

•

20 nov. 2016

par JOSE R

•

18 nov. 2017

Coursera Footer

Commencez ou faites progresser votre carrière

Parcourez les sujets populaires

Cours et articles populaires

Obtenez un diplôme ou un certificat en ligne

Coursera

Communauté

Plus

Apprendre partout
Placeholder
Placeholder
Placeholder