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

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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 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.


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.


201 - Machine Learning: Clustering & Retrieval 的 225 个评论(共 380 个)

创建者 Fernando B

Feb 21, 2017

Best Course on ML yet on the Web


Oct 17, 2020

It was brelient , just no words

创建者 Matheus F

Aug 10, 2018

Excelent course! Very helpful!

创建者 Ritik R S

Jun 3, 2022

Thank you so much! I love it.

创建者 Foo C S G

Mar 4, 2018

Tough slog, but well designed

创建者 Roger S

Sep 4, 2016

Worth the wait. COOL learning

创建者 Danylo D

Dec 6, 2016

Thank you, it was a good one

创建者 Sandeep J

Sep 4, 2016

Best course I've taken!! :)

创建者 Nirmal M

Jan 22, 2022

very helpful and inovating

创建者 Alessandro B

Dec 15, 2017

very useful and structured

创建者 wonjai c

May 19, 2020

difficult but good enough

创建者 Mostafa A

Aug 28, 2016

Fantastic course as usual

创建者 Gaurav K

Sep 23, 2020

very good course to do.

创建者 Jay M

May 26, 2020

Very good course for ML

创建者 Velpula M K

Dec 6, 2019

Good and best to learn.

创建者 Brian N

May 20, 2018

This course is exciting

创建者 Suryatapa R

Dec 16, 2016

It's an amazing Course.

创建者 Aishwarya A

Nov 28, 2020

best place to learn ML

创建者 Juan F H Z

Nov 15, 2018

The teacher is awesome

创建者 gaozhipeng

Dec 26, 2016


创建者 Zhongkai M

Feb 12, 2019

Great assignments : )

创建者 roi s

Oct 29, 2017

Great, very hands on!

创建者 Weituo H

Aug 29, 2016

strongly recommended!

创建者 Sukhvir S

Jul 10, 2020

wonderful experience

创建者 Omar S

Jul 12, 2017

I loved this course!