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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....



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.


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.


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


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 李紹弘

Aug 22, 2017

This course provides concise course.

創建者 Yong D K

May 07, 2018

This is the best course for Information Retrieval ever!

創建者 Ayan M

Dec 04, 2016

Excellent! Very good material and lectures and hands on. Really enriching.

創建者 zhenyue z

Aug 09, 2016

very nice lecture

創建者 Krisda L

Jul 19, 2017

Good overview of a lot of useful techniques.

創建者 Mark W

Aug 12, 2017

Excellent course. Emily and Carlos are fantastic teachers and have clearly put in a huge amount of effort in makign a great course. Thanks guys!

創建者 Pankaj K J

Oct 28, 2017

A great course to understand clustering as well as text mining. Lectures on KDD and LSH are equally important to understand and implement these algo . Many thanks

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


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 Cuiqing L

Nov 05, 2016


創建者 Nguyen D P

Feb 08, 2018

This is very useful course that can help me more to understand and resolve the complicated issue in the real world. I want to thank Coursera e-learning and the Washinton University for created this course that help people in the developing country like me can access the new technical.

創建者 Gillian P

Jul 23, 2017

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

創建者 Garvish

Jun 14, 2017

Great Information and organised course

創建者 Luiz C

Jul 10, 2018

An excellent Course. I was first doubtful about my interest for this Course, having already read mover Clustering. But this Course surprised me: it more than delivered, presented advanced concepts used in real world, always in a clear and engaging approach. The Tutor of this Course is a key component of my appreciation of this Course. To sum up, great content, great materials (Excellement videos, excellent slides, great assignemtns and quizzes - not a single bug!!!) in a very pleasant and engaging presentation. One work... THANK YOU