Chevron Left
Voltar para Machine Learning: Clustering & Retrieval

Comentários e feedback de alunos de Machine Learning: Clustering & Retrieval da instituição Universidade de Washington

4.7
estrelas
2,307 classificações

Sobre o 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....

Melhores avaliações

BK

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.

JM

16 de jan 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:

326 — 350 de 381 Avaliações para o Machine Learning: Clustering & Retrieval

por Marcin W

9 de ago de 2016

por Farrukh N A

17 de mar de 2017

por Iurii S

26 de nov de 2017

por Ayush K G

24 de fev de 2018

por Big O

21 de dez de 2018

por Evan

10 de out de 2021

por Michael B

4 de set de 2016

por Peter

26 de jul de 2016

por Hristo V

31 de ago de 2016

por Iñaki D R

14 de set de 2020

por stephane d

20 de abr de 2021

por Andrey T

11 de ago de 2016

por Charan S

30 de jul de 2017

por Jack B

3 de mar de 2017

por Mehul P

10 de set de 2017

por Adwait B

26 de jan de 2018

por Jayesh N J

25 de jan de 2022

por Pascal U E

20 de ago de 2016

por Dony A

5 de jan de 2017

por Galen S

8 de mai de 2017

por Koen O

27 de ago de 2017

por VYSHNAVI P

13 de dez de 2021

por Dhanasekar S

24 de dez de 2016

por Diego T B

28 de ago de 2016

por Sunil N

4 de jun de 2020