Machine Learning: Clustering & Retrieval に戻る

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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.

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by Subba R V O

•Jan 30, 2020

A great course, well organized and delivered with detailed info and examples. The quiz and the programming assignments are good and help in applying the course attended.

by Nelson P

•Dec 16, 2019

Excellent course. I liked the material and the assignments are great to consolidate the learning. I really liked the recap videos to solidify even more what I learned.

by Olga K

•Sep 23, 2016

Excellent course! Subjects are explained very well! Excellent quizzes that allow understanding of lectures better and excellent (challenging ) programming assignments.

by 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.

by 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

by 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.

by Ch S

•Feb 12, 2020

Excellent Course. This course provides in depth understanding of what's going in the background when an algorithm runs and how we can tune it for our purpose.

by Jay K S

•Jan 05, 2019

Excellent course material and fantastic delivery. You guys made this complex learning so simple and interesting . Thanks for all this, keep the good works.

by Dohyoung C

•Jun 04, 2019

Fascinating course…

LDA is little bit difficult to understand, but K-mean and Mixture models are easy to understand and quite important for clustering..

by Rama K R N R G

•Sep 09, 2017

Good presentation of topics. Detailed walk through of few advanced topics covered at the end would have been great. Felt the presentation went too fast.

by 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.

by 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.

by Jialie ( Y

•Feb 21, 2019

The course is really helpful, though it would be better for teacher to illustrate the concepts by using examples, instead of abstract terminologies

by 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!

by 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!

by 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.

by Tripat S

•Aug 07, 2016

This is the best course in ML - would recommend it ...the sequence of the courses is the best...the specialization in this ML is a career boost

by 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

by Jared C

•Aug 07, 2016

Exceptional course! This is challenging material for me, but it's presented in such a coherent manner that you can't help but absorb it.

by 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.

by Yao X

•Sep 30, 2019

Wish to have more detail on implementing the algorithm. Assignments are too easy for understanding the knowledge behind the scene.

by 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.

by 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.

by Robert C

•Feb 16, 2018

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

by Kuntal G

•Nov 03, 2016

Very Good in depth explanation and hand-on lab machine learning course. very focused on real world analytics and algorithms