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

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368 Bewertungen

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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

Aug 25, 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

Jan 17, 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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226 - 250 von 356 Bewertungen für Machine Learning: Clustering & Retrieval

von IDOWU H A

May 20, 2018

Excellent - Goo

von vivek k

May 25, 2017

awesome course!

von Bruno G E

Sep 03, 2016

Simply Amazing!

von Christopher D

Aug 09, 2016

Superb course!

von Jinho L

Sep 20, 2016

Great! thanks

von Pakomius Y N

Sep 29, 2020

Terima Kasih

von Divyanshu S

Aug 27, 2020

Very helpful

von JOYDIP M

Jul 30, 2020

very helpful

von Manikant R

Jun 21, 2020

Great course

von ANKUR S

Apr 14, 2020

loved it..!!

von Hanna L

Sep 02, 2019

Great class!

von Mark h

Aug 08, 2017

Very helpful

von 邓松

Jan 04, 2017

very helpful

von Jiancheng

Oct 27, 2016

Great intro!

von Thuong D H

Sep 23, 2016

Good course!

von Karundeep Y

Sep 18, 2016

Best Course.

von Saurabh A

Sep 24, 2020

very good !

von Pradeep N

Feb 22, 2017

"super one,

von clark.bourne

Jan 09, 2017

内容丰富实际,材料全。

von VITTE

Nov 11, 2018

Excellent.

von Gautam R

Oct 08, 2016

Awesome :)

von miguel s

Sep 20, 2020

very well

von Neha K

Sep 19, 2020

EXCELLENT

von PAWAN S

Sep 17, 2020

excellent

von Subhadip P

Aug 05, 2020

excellent