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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
estrelas
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

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.

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201 — 225 de 375 Avaliações para o Machine Learning: Clustering & Retrieval

por AANCHAL K

17 de Out de 2020

It was brelient , just no words

por Matheus F

10 de Ago de 2018

Excelent course! Very helpful!

por Foo C S G

4 de Mar de 2018

Tough slog, but well designed

por Roger S

4 de Set de 2016

Worth the wait. COOL learning

por Danylo D

6 de Dez de 2016

Thank you, it was a good one

por Sandeep J

4 de Set de 2016

Best course I've taken!! :)

por Nirmal M

22 de Jan de 2022

very helpful and inovating

por Alessandro B

15 de Dez de 2017

very useful and structured

por wonjai c

19 de Mai de 2020

difficult but good enough

por Mostafa A

28 de Ago de 2016

Fantastic course as usual

por Gaurav K

23 de Set de 2020

very good course to do.

por Jay M

26 de Mai de 2020

Very good course for ML

por Velpula M K

6 de Dez de 2019

Good and best to learn.

por Brian N

20 de Mai de 2018

This course is exciting

por Suryatapa R

16 de Dez de 2016

It's an amazing Course.

por Aishwarya A

28 de Nov de 2020

best place to learn ML

por Juan F H Z

15 de Nov de 2018

The teacher is awesome

por gaozhipeng

26 de Dez de 2016

VERY IMPRESSIVE COURSE

por Zhongkai M

12 de Fev de 2019

Great assignments : )

por roi s

29 de Out de 2017

Great, very hands on!

por Weituo H

29 de Ago de 2016

strongly recommended!

por Sukhvir S

10 de Jul de 2020

wonderful experience

por Omar S

12 de Jul de 2017

I loved this course!

por Itrat R

22 de Jan de 2017

Excellent Course!!!

por PAVITHRA B

29 de Set de 2020

VERY USEFUL COURSE