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Retour à Machine Learning: Clustering & Retrieval

Avis et commentaires pour d'étudiants pour Machine Learning: Clustering & Retrieval par Université de Washington

4.6
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2,193 évaluations
•
376 avis

À propos du cours

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....

Meilleurs avis

BK
24 août 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 janv. 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 sur 364 Avis pour Machine Learning: Clustering & Retrieval

par Pradeep N

•
21 févr. 2017

"super one,

par clark.bourne

•
8 janv. 2017

内容丰富实际，材料全。

par VITTE

•
11 nov. 2018

Excellent.

par Gautam R

•
8 oct. 2016

Awesome :)

par miguel s

•
20 sept. 2020

very well

par Neha K

•
19 sept. 2020

EXCELLENT

par PAWAN S

•
17 sept. 2020

excellent

par Subhadip P

•
4 août 2020

excellent

par Alan B

•
3 juil. 2020

Excellent

par RISHABH T

•
12 nov. 2017

excellent

par Iñigo C S

•
8 août 2016

Amazing.

par Mr. J

•
22 mai 2020

Superb.

par Zihan W

•
21 août 2020

great~

par Bingyan C

•
26 déc. 2016

great.

par Cuiqing L

•
5 nov. 2016

great!

par Job W

•
23 juil. 2016

Great!

par SUJAY P

•
21 août 2020

great

par Vaibhav K

•
29 sept. 2020

good

par PRITAM B

•
13 août 2020

well

par Frank

•
23 nov. 2016

非常棒！

par Pavithra M

•
24 mai 2020

nil

par Alexandre

•
23 oct. 2016

ok

par Nagendra K M R

•
10 nov. 2018

G

par Suneel M

•
9 mai 2018

E

par Lalithmohan S

•
26 mars 2018

V

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