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Voltar para Machine Learning: Clustering & Retrieval

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

4.7
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2,264 classificações
386 avaliações

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

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.

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.

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251 — 275 de 374 Avaliações para o Machine Learning: Clustering & Retrieval

por 邓松

4 de Jan de 2017

very helpful

por Jiancheng

26 de Out de 2016

Great intro!

por Thuong D H

22 de Set de 2016

Good course!

por Karundeep Y

18 de Set de 2016

Best Course.

por Prathibha A

6 de Dez de 2021

good course

por Siddharth V B

29 de Nov de 2020

nice course

por Saurabh A

24 de Set de 2020

very good !

por Pradeep N

21 de Fev de 2017

"super one,

por clark.bourne

8 de Jan de 2017

内容丰富实际,材料全。

por Salim T T

27 de Abr de 2021

Thank you!

por VITTE

11 de Nov de 2018

Excellent.

por Gautam R

8 de Out de 2016

Awesome :)

por miguel s

20 de Set de 2020

very well

por Neha K

19 de Set de 2020

EXCELLENT

por PAWAN S

17 de Set de 2020

excellent

por Subhadip P

4 de Ago de 2020

excellent

por Alan B

3 de Jul de 2020

Excellent

por RISHABH T

12 de Nov de 2017

excellent

por Iñigo C S

8 de Ago de 2016

Amazing.

por Mr. J

22 de Mai de 2020

Superb.

por Zihan W

21 de Ago de 2020

great~

por Bingyan C

26 de Dez de 2016

great.

por Cuiqing L

5 de Nov de 2016

great!

por Job W

23 de Jul de 2016

Great!

por SUJAY P

21 de Ago de 2020

great