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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.


151 - Machine Learning: Clustering & Retrieval 的 175 個評論(共 295 個)

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 陈佳艺

May 17, 2017

sometimes difficult,but import so many useful knowledge

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 José M T

Apr 14, 2017

The teachers are really amazing. They do not just explain it as if they read a book. They explain the concepts very well


May 07, 2017

very good! strongly recommend to people who want to start a career on data science or are interested in it

創建者 Sameer M

Sep 19, 2017

Excellent course! must for machine learning beginners!!

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.

創建者 Lalithmohan S

Mar 26, 2018


創建者 Foo C S G

Mar 04, 2018

Tough slog, but well designed

創建者 Yang X

Nov 15, 2017

Thank you Emily and Carlos! You guys are amazing!!!


May 20, 2018

Excellent - Goo

創建者 Sean S

Apr 03, 2018

Another great course and sadly the last of this specialization. I found the material for this course to be the most challenging yet, specifically the LDA module. The programming assignments were all very manageable thanks to graphlab and the very explicit hints provided but I do not feel like I reached the same level of understanding as I did for the previous courses in the specialization. I have grown to enjoy using graphlab and would likely use it going forward if not for the licensing. I am very disappointed that the remaining courses will not be offered and am now in search for another great machine learning resource.

創建者 Alvaro M M

Jan 07, 2018

I liked it a lot. My only problem was to get the GraphLab to work here. Loved the option to download the videos and material before and the content is awesome.

創建者 Suneel M

May 09, 2018


創建者 Swati D

May 02, 2018

This course is a very structured and progressive learning. It is an advantage , if we know python . However, one can still manage and explore Machine learning and Deep learning concept of AI. The case study and real life approach keeps your quest on. This is a great initiative and gives us an opportunity to be future ready while at job. Many notion went wrong about AI and the chapters are well designed to keep us engaged while we

創建者 Shuang D

Jun 29, 2018

advanced knowledge on ML, great course

創建者 Brian N

May 20, 2018

This course is exciting

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!