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....

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.

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.

篩選依據：

創建者 Miao J

•Jul 01, 2016

Another great course. Strongly recommend!

創建者 Justin K

•Aug 17, 2016

An interesting topic, presented well by the instructor and reinforced by intermediate-level programming assignments.

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 Usman

•Nov 28, 2016

This was another great course. I hope that the instructors indulge in a little bit more theory. Anyway it was a magnificent course. Hope the coming courses are as good as this one.

創建者 krishna k s

•Apr 20, 2018

This is very nice and interesting course. It gives practical application of machine learning application. I would consider this course as applied machine learning course as it lacks mathematical intuition. Nevertheless, course it great and cover major points in the machine learning field.

創建者 Sumit

•Sep 17, 2016

Excellent course

創建者 Alfred D

•Mar 24, 2018

KD trees, LSH along with LDH were some real deep techniques I've learnt and benefitted.

Thanks a ton to Emily and Carlos , you guys are amazing teachers for such a complex subject as ML and the algorithms it consists of .

創建者 Saqib N S

•Dec 05, 2016

The course dived into basic and advanced concepts of unsupervised learning. As before, Prof Fox did a great job at explaining things.

創建者 Russell H

•Oct 09, 2016

Detailed coverage of several approaches to clustering. Not easy but learned a lot.

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 Christopher A

•Oct 01, 2016

The best course in the specialization thus far. Very rich and wide ranging, perfect for the motivated part-time learner who wants to be challenged and have ample reason to revisit the material. I only wish this course had been longer, perhaps shortening the classification course to make room.

創建者 Alexandre

•Oct 23, 2016

ok

創建者 Andrey N

•Mar 12, 2017

Some themes are shown very superficially it would be great to go deeper. Despite of this the course is great!

Thanks.

創建者 João F A d S

•Aug 07, 2016

Great course. Well packed, well explained, nice practical examples, good all around MOOC with of info.

創建者 Snehotosh K B

•Dec 03, 2016

Best course available till date as MooC

創建者 Nitish V

•Oct 29, 2017

The Course is good . Covered lots of topics .

創建者 Robi s

•Sep 18, 2017

Great instruction, great course, and provide information I used directly in my work.

創建者 Diogo J A P

•Jan 25, 2017

The material is complex and challenging, but the teaching procedure is carefully thought out in a way that you quickly get it, giving you a great sense of accomplishment.

創建者 Bingyan C

•Dec 27, 2016

great.

創建者 Uday A

•Aug 13, 2017

Thank you so much, Emily and Carlos! Really liked all the courses, and I daresay these are the best ML courses available online. Very insightful, and also cover the mathematical part of the algorithms. Since there are now just 4 courses in this ML Specialization, I would mostly jump to Andrew Ng's new Deep Learning Specialization for further studies. But will look out for your remaining courses to be available once more. If and when they come out, it would be great to send out a notification. Thanks!

創建者 Danylo D

•Dec 06, 2016

Thank you, it was a good one

創建者 Bruno C K

•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.

創建者 Robert C

•Feb 16, 2018

Emily was fantastic at explaining difficult to understand concepts. Thoroughly enjoyed the course, and learned quite a lot.