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學生對 华盛顿大学 提供的 Machine Learning: Clustering & Retrieval 的評價和反饋

4.6
1,796 個評分
305 個審閱

課程概述

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....

熱門審閱

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.

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.

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176 - Machine Learning: Clustering & Retrieval 的 200 個評論(共 295 個)

創建者 Aditi R

Dec 25, 2016

This course contain many advance topic which was covered in fast pace by the professor special end lectures. This course contain very important topics of Machine learning could have given more time in explaining things. Thanks professor

創建者 Nada M

Jun 11, 2017

Thank you! I loved all your classes.

創建者 Mohd A

Aug 14, 2016

This is the toughest courses in the specialization so far. But if you manage to complete it, you'll have some really advance skills under your belt.

創建者 Jorge L

May 26, 2017

I'm a grad student and I can notice the instructor makes a difference in this course. I fully recommend it.

創建者 JiHe

Sep 08, 2016

Very good course!

創建者 Frank

Nov 23, 2016

非常棒!

創建者 Marcio R

Sep 02, 2016

Following the overall quality of this Specialization, this course was excellent. From the content, to the assesments, material and teachers. This course is a really good starting point to become an expert in Machine Learning techniques.

創建者 Rahul G

Jun 13, 2017

Good course but Week 5 LDA needs improvement.

創建者 Dmitri T

Dec 05, 2016

Great course! Very simple and practical.

創建者 Ben L

Jun 11, 2017

The most challenging of the four courses in the specialization.

創建者 Iñigo C S

Aug 08, 2016

Amazing.

創建者 Daniel R

Aug 17, 2016

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

創建者 Songxiang L

Dec 04, 2016

Very good, not only learn many good ML concepts, but also polish my python programming skill a lot. Thank you, Emily and Carlos.

創建者 Kevin C N

Mar 26, 2017

E

創建者 Feng G

Aug 09, 2018

Emily is an extremely awesome instructor. For those who have some background in statistics, biostats , econometrics and math and want to study machine learning by themselves, these modules can be an outline that introduce basic topics in machine learning.

I'm looking forward to see more advanced courses in these topics from Carlos and Emily.

創建者 Matheus F

Aug 11, 2018

Excelent course! Very helpful!

創建者 Arun K P

Oct 27, 2018

Very useful and informative .It help and provide confidence to the job more effectively. Thanks for the help and good cour

創建者 Yugandhar D

Oct 29, 2018

Excellent course on clustering and retreival. The assignments were thorough and productive.

創建者 Fahad S

Nov 03, 2018

Emily ross is an amazing instructor. The course introduces many complex topics and presents them intuitively.

創建者 VITTE

Nov 11, 2018

Excellent.

創建者 Susree S M

Nov 14, 2018

This course is very useful to know about the concepts of machine learning and do hands-on activities.

創建者 Juan F H

Nov 15, 2018

The teacher is awesome

創建者 Somu P

Nov 17, 2018

Excellent course, which gives you all you need to learn about machine learning. Concepts and hands on practical ex

創建者 Nagendra K M R

Nov 11, 2018

G

創建者 Manoj K

Nov 26, 2018

session was very helpful & full with relevant contents