Chevron Left
Retour à Machine Learning: Clustering & Retrieval

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

4.6
étoiles
1,958 évaluations
334 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

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.

Filtrer par :

176 - 200 sur 322 Avis pour Machine Learning: Clustering & Retrieval

par Garvish

Jun 14, 2017

Great Information and organised course

par Ce J

Jun 26, 2017

well organized and easy to understand

par 李紹弘

Aug 22, 2017

This course provides concise course.

par Nada M

Jun 11, 2017

Thank you! I loved all your classes.

par Fernando B

Feb 21, 2017

Best Course on ML yet on the Web

par Matheus F

Aug 11, 2018

Excelent course! Very helpful!

par Foo C S G

Mar 04, 2018

Tough slog, but well designed

par Roger S

Sep 04, 2016

Worth the wait. COOL learning

par Danylo D

Dec 06, 2016

Thank you, it was a good one

par Sandeep J

Sep 04, 2016

Best course I've taken!! :)

par Alessandro B

Dec 15, 2017

very useful and structured

par wonjai c

May 19, 2020

difficult but good enough

par Mostafa A M

Aug 28, 2016

Fantastic course as usual

par Jay M

May 26, 2020

Very good course for ML

par Velpula M K

Dec 06, 2019

Good and best to learn.

par Brian N

May 20, 2018

This course is exciting

par suryatapa r

Dec 16, 2016

It's an amazing Course.

par Juan F H

Nov 15, 2018

The teacher is awesome

par gaozhipeng

Dec 27, 2016

VERY IMPRESSIVE COURSE

par Zhongkai M

Feb 12, 2019

Great assignments : )

par roi s

Oct 29, 2017

Great, very hands on!

par Weituo H

Aug 29, 2016

strongly recommended!

par Omar S

Jul 12, 2017

I loved this course!

par Itrat R

Jan 23, 2017

Excellent Course!!!

par SUBBA R D

Jun 16, 2020

most useful course