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Kursteilnehmer-Bewertung und -Feedback für Machine Learning: Clustering & Retrieval von University of Washington

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383 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

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.

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.

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251 - 275 von 371 Bewertungen für Machine Learning: Clustering & Retrieval

von Jiancheng

26. Okt. 2016

Great intro!

von Thuong D H

22. Sep. 2016

Good course!

von Karundeep Y

18. Sep. 2016

Best Course.

von Siddharth V B

29. Nov. 2020

nice course

von Saurabh A

24. Sep. 2020

very good !

von Pradeep N

21. Feb. 2017

"super one,

von clark.bourne

8. Jan. 2017

内容丰富实际,材料全。

von Salim T T

27. Apr. 2021

Thank you!

von VITTE

11. Nov. 2018

Excellent.

von Gautam R

8. Okt. 2016

Awesome :)

von miguel s

20. Sep. 2020

very well

von Neha K

19. Sep. 2020

EXCELLENT

von PAWAN S

17. Sep. 2020

excellent

von Subhadip P

4. Aug. 2020

excellent

von Alan B

3. Juli 2020

Excellent

von RISHABH T

12. Nov. 2017

excellent

von Iñigo C S

8. Aug. 2016

Amazing.

von Mr. J

22. Mai 2020

Superb.

von Zihan W

21. Aug. 2020

great~

von Bingyan C

26. Dez. 2016

great.

von Cuiqing L

5. Nov. 2016

great!

von Job W

23. Juli 2016

Great!

von SUJAY P

21. Aug. 2020

great

von Vaibhav K

29. Sep. 2020

good

von Pritam B

13. Aug. 2020

well