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Bewertung und Feedback des Lernenden für Machine Learning: Clustering & Retrieval von University of Washington

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2,296 Bewertungen
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392 Bewertungen

Über den Kurs

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....

Top-Bewertungen

JM

16. Jan. 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. Aug. 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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226 - 250 von 380 Bewertungen für Machine Learning: Clustering & Retrieval

von Itrat R

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22. Jan. 2017

Excellent Course!!!

von PAVITHRA B

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29. Sep. 2020

VERY USEFUL COURSE

von SUBBA R D

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16. Juni 2020

most useful course

von Israel C

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15. Aug. 2017

Excellent Course!

von Antonio P L

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3. Okt. 2016

Excellent course.

von Ji H

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8. Sep. 2016

Very good course!

von Igor D

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21. Aug. 2016

This was AWESOME!

von zhenyue z

•

9. Aug. 2016

very nice lecture

von Anurag B

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20. Dez. 2019

Great Experience

von Xue

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18. Dez. 2018

Great but hard~!

von 嵇昊雨

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25. Apr. 2017

内容深度可以，对个人的帮助比较大

von Daniel W

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23. Dez. 2016

Excellent course

von Sumit

•

17. Sep. 2016

Excellent course

von Phan T B

•

8. Aug. 2016

very good course

von Md. K H T

•

25. Juli 2020

Awesome Course.

von IDOWU H A

•

20. Mai 2018

Excellent - Goo

von vivek k

•

25. Mai 2017

awesome course!

von Bruno G E

•

3. Sep. 2016

Simply Amazing!

von Christopher D

•

9. Aug. 2016

Superb course!

von Jinho L

•

20. Sep. 2016

Great! thanks

von Sumit K J

•

24. Jan. 2021

Great Course

von Pakomius Y N

•

28. Sep. 2020

Terima Kasih

von Divyanshu S

•

27. Aug. 2020

Very helpful

von JOYDIP M

•

30. Juli 2020

very helpful

von Manikant R

•

21. Juni 2020

Great course

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