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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

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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....



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.


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.


126 - Machine Learning: Clustering & Retrieval 的 150 個評論(共 295 個)

創建者 Muhammad H A

Aug 13, 2016

I used to run into a wall at work trying to train models with recursive partitioning or neural nets because of the long time they took to train for high dimensional data. These clustering techniques are an immense help.

Awesome course, with a brilliant instructor and brilliant assignments.

創建者 Veer A S

Mar 24, 2018

Very informative and interesting course.

創建者 shaonan

Nov 20, 2016

Deep insight into most useful techniques of machine learning.

創建者 Olga K

Sep 23, 2016

Excellent course! Subjects are explained very well! Excellent quizzes that allow understanding of lectures better and excellent (challenging ) programming assignments.

創建者 Saint-Clair d C L

Aug 30, 2016

This course has been an amazing experience. Congrats to you, Carlos and Emmy!

創建者 Frank

Nov 23, 2016


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 Dmitri T

Dec 05, 2016

Great course! Very simple and practical.

創建者 Rahul G

Jun 13, 2017

Good course but Week 5 LDA needs improvement.

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


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 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 Patrick M

Aug 09, 2016

Excellent course. Nice selection of algorithms reviewed - all clearly explained with sample implementations.

創建者 JiHe

Sep 08, 2016

Very good course!

創建者 Itrat R

Jan 23, 2017

Excellent Course!!!

創建者 Geoff B

Jul 14, 2016

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

創建者 Naman A

Mar 05, 2017

The course was very engaging and mathematically intensive. It provides background for all the necessary tools one must know to start clustering. Great Course. Will miss this course format as 5 and 6 are cancelled!

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 Sean L

Oct 04, 2016

wonderful course for beginner of machine learning.

創建者 Jiancheng

Oct 27, 2016

Great intro!

創建者 Etienne V

Feb 19, 2017

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