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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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126 - 150 von 360 Bewertungen für Machine Learning: Clustering & Retrieval

von Ayan M

4. Dez. 2016

Excellent! Very good material and lectures and hands on. Really enriching.

von Amey B

18. Dez. 2016

Very Insightful. Great Instructors. Awesome Forum and intelligible peers.

von Muhammad Z H

30. Aug. 2019

Machine Learning: Clustering & Retrieval, I have learned a lot professor

von YASHKUMAR R T

31. Mai 2019

Awesome course to understand the concept behind Gaussian Mixture model.

von Edwin P

15. Feb. 2019

Excellent, good contribution to the technical and practical knowledge ML

von Parab N S

12. Okt. 2019

Excellent course on clustering & retrieval by University of Washington

von Manuel A

8. Sep. 2019

Great course and specialization overall, both lectures and assignments

von Prabhu

2. Nov. 2019

Very clear explanation of concepts with a good selection of examples.

von Hans H

27. Juli 2018

Amazing course, I´ve learned so much stuff that I can use in my job.

von Swapnil A

6. Sep. 2020

Really awesome course. Dr. Emily explains everything from scratch.

von Jonathan H

1. Juli 2017

Emily is great! Excellent course that covers a ton of material!!!

von johny v o

21. Nov. 2020

very helpfull the course, congrat!!! and thank u for this course

von Yihong C

30. Sep. 2016

a practical and interesting course about clustering and retrival

von Ben L

10. Juni 2017

The most challenging of the four courses in the specialization.

von Eric N

11. Okt. 2020

Excellent online teaching with clear and concise explanations!

von Akash G

11. März 2019

Machine Learning: Clustering & Retrieval good and learn easily

von shaonan

20. Nov. 2016

Deep insight into most useful techniques of machine learning.

von JOSE R

18. Nov. 2017

Very well explained. The LDA was difficult to learn. Thanks.

von Daniel R

16. Aug. 2016

Another great hit by Emily and Carlos!!! Excellent Course!!!

von Yifei L

30. Juli 2016

Good course for KD trees, LSH, Gaussian mixed model and LDA.

von Victor C

24. Juni 2017

Excellent teacher and material. I wish there were more...

von Moayyad A Y

4. Dez. 2016

this is not a an easy course but certainly an awesome one

von Fengchen G

2. Sep. 2016

Awesome course! The session on EM algorithm is revealing!

von Divyang S

13. Sep. 2020

Excellent content... Really intuitive and well explained

von Yong D K

7. Mai 2018

This is the best course for Information Retrieval ever!