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

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370 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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151 - 175 von 360 Bewertungen für Machine Learning: Clustering & Retrieval

von Sameer M

19. Sep. 2017

Excellent course! must for machine learning beginners!!

von 陈佳艺

17. Mai 2017

sometimes difficult,but import so many useful knowledge

von 백원광

16. Jan. 2017

Very sophisticated, friendly and practical instructions

von Manoj K

26. Nov. 2018

session was very helpful & full with relevant contents

von Siwei Y

17. Jan. 2017

本来不报什么期望,但是该门课确实做得相当好。 相信该课的老师们花了巨大的心血。真的是业界良心。所以强烈点赞。

von Oleg B

3. Dez. 2016

Great course, very hands-on, very practical knowledge.

von KAI N

3. Jan. 2019

Excellent course with great and reachable explanation

von Vladimir V

27. Juni 2017

Awesome course. Thank you Emily, Carlos and Coursera!

von Kishore P V

5. Okt. 2016

One of the best machine learning course I have taken.

von Jaswant J

31. März 2017

Very nice course. Concepts are covered very clearly.

von Yang X

14. Nov. 2017

Thank you Emily and Carlos! You guys are amazing!!!

von Sean L

4. Okt. 2016

wonderful course for beginner of machine learning.

von Banka C G

10. Aug. 2019

Its my great experience for step by step modules

von Yufeng X

9. Juli 2019

It opened the door to more advanced techniques.

von Anmol G

16. Dez. 2016

So Much Concepts to learn and totally worth it!

von seokwon y

26. Juli 2018

good to learn what is clustering and retrieval

von Arash A

5. Jan. 2017

Enjoyed the course and learned a lot. Amazing!

von David F

21. Okt. 2016

Excellent course - and of great practical use.

von Nitish V

29. Okt. 2017

The Course is good . Covered lots of topics .

von Rahul G

13. Juni 2017

Good course but Week 5 LDA needs improvement.

von Stanislav B

15. Apr. 2020

one of the best courses Ive seen on coursera

von Jason G

9. Aug. 2017

Harder than the previous ones, but enjoyable

von Krisda L

19. Juli 2017

Good overview of a lot of useful techniques.

von felix a f a

8. Aug. 2016

less complex exercises to check and validate

von Feiwen C ( C I

1. Juni 2017

Good course. Learned a lot from it. Thanks!