Chevron Left
Volver a Machine Learning: Clustering & Retrieval

Opiniones y comentarios de aprendices correspondientes a Machine Learning: Clustering & Retrieval por parte de Universidad de Washington

2,307 calificaciones

Acerca del Curso

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....

Principales reseñas


24 de ago. de 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.


16 de ene. de 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.

Filtrar por:

226 - 250 de 381 revisiones para Machine Learning: Clustering & Retrieval

por Omar S

12 de jul. de 2017

por Itrat R

22 de ene. de 2017


29 de sep. de 2020


16 de jun. de 2020

por Manuel I C M

15 de ago. de 2017

por Antonio P L

3 de oct. de 2016

por Ji H

8 de sep. de 2016

por Igor D

21 de ago. de 2016

por zhenyue z

9 de ago. de 2016

por Anurag B

20 de dic. de 2019

por Xue

18 de dic. de 2018

por 嵇昊雨

25 de abr. de 2017

por Daniel W

23 de dic. de 2016

por Sumit

17 de sep. de 2016

por Phan T B

8 de ago. de 2016

por Md. K H T

25 de jul. de 2020


20 de may. de 2018

por vivek k

25 de may. de 2017

por Bruno G E

3 de sep. de 2016

por Christopher D

9 de ago. de 2016

por Jinho L

20 de sep. de 2016

por Sumit K J

24 de ene. de 2021

por Pakomius Y N

28 de sep. de 2020

por Divyanshu S

27 de ago. de 2020


30 de jul. de 2020