返回到 Machine Learning: Clustering & Retrieval

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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.

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創建者 Suresh K P

•Dec 21, 2017

Interesting, lot of Algorithms and methods to use iin upcoming projects and real time applications

創建者 Alessandro B

•Dec 15, 2017

very useful and structured

創建者 Ruchi S

•Jan 24, 2018

E

創建者 Dongliang Z

•Mar 22, 2018

I enjoyed this course. This specialization is very good for machine learning beginner. Look forward to the next course anyway.

創建者 Phil B

•Feb 13, 2018

Again the lecturing style and course content were excellent, allowing us to write fairly complex functions to implement our own algorithms from scratch but also using pre-built functions when necessary to allow us to explore the effects of different variables. The benefits and costs of the different types of clustering were clearly stated. It's a shame that the specialization stops here, as a capstone project with the same quality of these 4 courses would really provide the students with something they can show off to potential employers. The problem most students will have when coming off this specialization is how to implement and deploy your own model into a service like a website.

創建者 Suneel M

•May 09, 2018

E

創建者 Manuel T F

•Sep 24, 2017

Since I took the courses 1, 2 and 3 of this series, I really enjoyed this fourth part a lot!

Now I'm really looking forward to do some clustering!

創建者 Fernando B

•Feb 21, 2017

Best Course on ML yet on the Web

創建者 gaston F

•Oct 11, 2016

This course was awesome as all the previous courses, I'm waiting to the next course and the capstone

創建者 Manuel S

•Oct 01, 2016

Amazing course, really helpful, as a ML researcher you need this kind of foundation

創建者 Liling T

•Aug 15, 2016

Emily Fox did a great job in explaining tough concepts with simple explanation of the components in the formulas!!

It's a little tough to get through the materials though, it's the 4th course in University of Washington's machine learning specialization afterall =)

創建者 Shaowei P

•Aug 08, 2016

very good course but the last few topics could be improved with better assignments that could be broken down into smaller sub assignments

創建者 Vaidas A

•May 29, 2017

This course was great! With good code examples and algorithm applications and also intuition!

It's a shame that we couldn't finish planned courses due to busy schedules of instructors as I was really looking forward to the capstone project!

創建者 Igor D

•Aug 21, 2016

This was AWESOME!

創建者 Mostafa A M

•Aug 28, 2016

Fantastic course as usual

創建者 Antonio P L

•Oct 03, 2016

Excellent course.

創建者 Moises V

•Oct 30, 2016

I loved this course. then content is designed to acquire strong foundations in clustering.

創建者 Atul A

•Aug 25, 2017

Great course. Different from earlier courses in the Specialization, this course is quite challenging in both theory and practice. However, it is super important, as clustering is all around us in real-world data.

Worth it!

創建者 Oleg B

•Dec 04, 2016

Great course, very hands-on, very practical knowledge.

創建者 Chandrashekar T

•Oct 11, 2016

The material covered in this course is immense and gives a deep understanding of several algorithms required to perform unsupervised learning tasks.

創建者 Thuong D H

•Sep 23, 2016

Good course!

創建者 vivek k

•May 25, 2017

awesome course!

創建者 Pankaj K

•Sep 08, 2017

Great course, all the explanations are so good and well explained in the slides. Programming assignments are pretty challenging, but give really good insight into the algorithms!.

Thanks!

創建者 Miguel P

•Jul 13, 2016

I loved the previous 3 courses and what I saw in this course so far seems pretty interesting. I'm really sad that Coursera decided to block access to assignments for not paying users. I really wanted to continue with the specialization but I already purchased another specialization, so I'm going to have to put this course on hold for a now.

創建者 Vladimir V

•Jun 27, 2017

Awesome course. Thank you Emily, Carlos and Coursera!