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

2,316 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....



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.


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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201 - 225 von 383 Bewertungen für Machine Learning: Clustering & Retrieval

von 李紹弘

22. Aug. 2017

This course provides concise course.

von Nada M

11. Juni 2017

Thank you! I loved all your classes.

von Fernando B

21. Feb. 2017

Best Course on ML yet on the Web


17. Okt. 2020

It was brelient , just no words

von Matheus F

10. Aug. 2018

Excelent course! Very helpful!

von Ritik R S

3. Juni 2022

Thank you so much! I love it.

von Foo C S G

4. März 2018

Tough slog, but well designed

von Roger S

4. Sep. 2016

Worth the wait. COOL learning

von dan o

6. Dez. 2016

Thank you, it was a good one

von Sandeep J

4. Sep. 2016

Best course I've taken!! :)

von Nirmal M

22. Jan. 2022

very helpful and inovating

von Alessandro B

15. Dez. 2017

very useful and structured

von Adapa S K

23. Juli 2022

quality ofcontent is good

von wonjai c

19. Mai 2020

difficult but good enough

von Mostafa A

28. Aug. 2016

Fantastic course as usual

von Gaurav K

23. Sep. 2020

very good course to do.

von Jay M

26. Mai 2020

Very good course for ML

von Velpula M K

6. Dez. 2019

Good and best to learn.

von Brian N

20. Mai 2018

This course is exciting

von Suryatapa R

16. Dez. 2016

It's an amazing Course.

von Aishwarya A

28. Nov. 2020

best place to learn ML

von Juan F H Z

15. Nov. 2018

The teacher is awesome

von gaozhipeng

26. Dez. 2016


von Zhongkai M

12. Feb. 2019

Great assignments : )

von roi s

29. Okt. 2017

Great, very hands on!