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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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383 条评论


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.


226 - Machine Learning: Clustering & Retrieval 的 250 个评论(共 371 个)

创建者 Antonio P L

Oct 3, 2016

Excellent course.

创建者 jihe

Sep 8, 2016

Very good course!

创建者 Igor D

Aug 21, 2016

This was AWESOME!

创建者 zhenyue z

Aug 9, 2016

very nice lecture

创建者 Anurag B

Dec 20, 2019

Great Experience

创建者 Xue

Dec 18, 2018

Great but hard~!

创建者 嵇昊雨

Apr 25, 2017


创建者 Daniel W

Dec 23, 2016

Excellent course

创建者 Sumit

Sep 17, 2016

Excellent course

创建者 Phan T B

Aug 8, 2016

very good course

创建者 Md. K H T

Jul 25, 2020

Awesome Course.


May 20, 2018

Excellent - Goo

创建者 vivek k

May 25, 2017

awesome course!

创建者 Bruno G E

Sep 3, 2016

Simply Amazing!

创建者 Christopher D

Aug 9, 2016

Superb course!

创建者 Jinho L

Sep 20, 2016

Great! thanks

创建者 Sumit K J

Jan 24, 2021

Great Course

创建者 Pakomius Y N

Sep 28, 2020

Terima Kasih

创建者 Divyanshu S

Aug 27, 2020

Very helpful


Jul 30, 2020

very helpful

创建者 Manikant R

Jun 21, 2020

Great course


Apr 14, 2020

loved it..!!

创建者 Hanna L

Sep 2, 2019

Great class!

创建者 Mark h

Aug 8, 2017

Very helpful

创建者 邓松

Jan 4, 2017

very helpful