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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.


101 - Machine Learning: Clustering & Retrieval 的 125 個評論(共 293 個)

創建者 Jaswant J

Mar 31, 2017

Very nice course. Concepts are covered very clearly.


Nov 12, 2017


創建者 Christopher D

Aug 09, 2016

Superb course!

創建者 Rohan K

Mar 22, 2018

Good introduction to very complicated concepts. I now have the tools to learn more about HHMs and anomaly detection.

創建者 MARC G

Oct 21, 2017

Clear and well designed course. The assignments are quite thorough. Sometimes, quiz question are not so clear though.

創建者 Roger S

Sep 04, 2016

Worth the wait. COOL learning

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 陈佳艺

May 17, 2017

sometimes difficult,but import so many useful knowledge

創建者 Sally M

Jan 02, 2017

Great course but hard going at times for those of us without a strong maths background. The assignments took me a long time to complete and I think I'll have to revisit some areas as I become more familiar with them to really get the full benefit.

創建者 Daniel W

Dec 23, 2016

Excellent course

創建者 Kate S

Jun 30, 2017

I really enjoyed and learned a lot from this class. It made me interested to go out and learn other machine learning methods which are derived from what was taught.

創建者 Mark h

Aug 08, 2017

Very helpful

創建者 Job W

Jul 23, 2016


創建者 Amey B

Dec 18, 2016

Very Insightful. Great Instructors. Awesome Forum and intelligible peers.

創建者 邓松

Jan 04, 2017

very helpful

創建者 David F

Oct 21, 2016

Excellent course - and of great practical use.

創建者 Brandon H

Dec 14, 2016

This was probably the most challenging course of them all, I thoroughly enjoyed it! Looking forward to dimensionality reduction and the capstone.

創建者 Samuel d Z

Jul 18, 2017

Brilliant, anyone interested to get proficient in Data Science and Machine Learning need to take this course. It is well structured and although very challenging at times, it is always possible to get the right result.

創建者 Jonathan H

Jul 01, 2017

Emily is great! Excellent course that covers a ton of material!!!

創建者 Prasant K S

Dec 21, 2016

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

創建者 Feiwen C ( C I

Jun 02, 2017

Good course. Learned a lot from it. Thanks!

創建者 Siwei Y

Jan 17, 2017

本来不报什么期望,但是该门课确实做得相当好。 相信该课的老师们花了巨大的心血。真的是业界良心。所以强烈点赞。

創建者 roi s

Oct 29, 2017

Great, very hands on!

創建者 Ferenc F P

Jan 25, 2018

Very good course. Even though I had some machine learning background, this course provided new insights and new algorithms, like KDTree, Locally Sensitive Hashing, Latent Dirichlet Allocation, and mixture of Gaussians. the only drawback is that with scikitlearn, not always you get the same results as with GraphLab. Thanks for the instructors for this great specialization.