Chevron Left
Voltar para Machine Learning: Clustering & Retrieval

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

4.6
estrelas
2,301 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:

26 — 50 de 381 Avaliações para o Machine Learning: Clustering & Retrieval

por Ferenc F P

25 de jan de 2018

por Stephen G

13 de ago de 2016

por Feng G

8 de ago de 2018

por Miguel P

13 de jul de 2016

por Alessio D M

1 de ago de 2016

por Muhammad W K

22 de out de 2019

por Christopher A

1 de out de 2016

por Krishna K

20 de abr de 2018

por Muhammad H A

13 de ago de 2016

por Phuong N

7 de fev de 2018

por Renato R R

4 de jan de 2018

por Liling T

15 de ago de 2016

por Martin R

12 de dez de 2018

por Kumiko K

14 de ago de 2016

por Sally M

2 de jan de 2017

por Michael B

12 de jul de 2016

por Vaidas A

29 de mai de 2017

por Bhavesh G

12 de mai de 2020

por Aditi R

25 de dez de 2016

por Marcio R

2 de set de 2016

por Jafed E G

6 de jul de 2019

por Mohamed A H

19 de jun de 2019

por Alfred D

24 de mar de 2018

por Atul A

25 de ago de 2017

por Samuel d Z

18 de jul de 2017