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返回到 Machine Learning: Clustering & Retrieval

学生对 华盛顿大学 提供的 Machine Learning: Clustering & Retrieval 的评价和反馈

2,299 个评分


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....



Jan 16, 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.


Aug 24, 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.


101 - Machine Learning: Clustering & Retrieval 的 125 个评论(共 381 个)

创建者 Cristian A G F

Dec 30, 2020

In general, all of the courses were awesome because of the methodology used by the professors. Thank you!

创建者 Prasant K S

Dec 20, 2016

It is explained in simple and lucid language by expert Emily and codes illustrated by Carlos. Go for it.

创建者 João S

Aug 7, 2016

Great course. Well packed, well explained, nice practical examples, good all around MOOC with of info.

创建者 Geoff B

Jul 14, 2016

Another great introduction. The assignments are notably a little bit harder than the previous courses.

创建者 Susree S M

Nov 14, 2018

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

创建者 Viktor K

May 14, 2021

The explanation was really good, and now, I find it so simple to use Machine Learning. Thanks a lot!

创建者 Gaston F

Oct 10, 2016

This course was awesome as all the previous courses, I'm waiting to the next course and the capstone

创建者 Sayan B

Dec 5, 2019

This is actually a tremendous course. Assignments are not so good, but the materials are wonderful.

创建者 Suresh K P

Dec 21, 2017

Interesting, lot of Algorithms and methods to use iin upcoming projects and real time applications

创建者 Gillian P

Jul 23, 2017

A very good course with two engaging and sympathetic teachers. Would love to see the next courses

创建者 Neemesh J

Oct 28, 2019

Coursera is the best learning app. I am really thankful for getting very good training lectures.

创建者 Etienne V

Feb 19, 2017

Excellent course! Thanks a lot for the effort in compiling this course... I really enjoyed it!

创建者 Aakash S

Jun 18, 2019

Such a clear explanation of topics of clustering. Without doubt one of the best in business.

创建者 Renato R S

Aug 27, 2016

A perfect and balanced introduction to the subjects, adding theory and practice beautifully.

创建者 Noor A K

Jul 4, 2020

I don't know that there was some prerequisite of python.

Please unenroll me from this course

创建者 Yugandhar D

Oct 29, 2018

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

创建者 Sathiraju E

Mar 3, 2019

Very nice course. Things are well explained, however some concepts could be expanded more.

创建者 Moises V

Oct 30, 2016

I loved this course. then content is designed to acquire strong foundations in clustering.

创建者 Yi W

Sep 27, 2016

As someone very keen on math, more math background as optimal video would be more helpful.

创建者 Priyanshu R S

Nov 27, 2020

These are amazing courses. A big big thanks to the team for making me more knowledgeable.

创建者 austin

Aug 9, 2017

Awesome course. Very detailed and thorough, and the bonus sections are really useful too.

创建者 Val V

Apr 8, 2021

Very well presented. I've throughly enjoyed the course and feel like I've learned a ton.

创建者 B P S

May 27, 2020

It helped me to give concepts of machine learning and clustering techniques and modules.

创建者 Venkateshwaralu S

Aug 7, 2016

Sets a new benchmark for the specialization !!! A great offering on Machine Learning :)

创建者 Jifu Z

Jul 22, 2016

Good class, But it would be much better if the quiz is open to those who doesn't pay.