Machine Learning: Clustering & Retrieval

This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.

( 287 Reviews )

Created by: Emily Fox

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Overall Score : 90 / 100

Course Description

Case Studies: Finding Similar DocumentsA 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.

Time: 47h

Instructor Details

Emily Fox

4.5 Rating
287 Reviews

Emily Fox is an assistant professor and the Amazon Professor of Machine Learning in the Statistics Department at the University of Washington. She was formerly at the Wharton Statistics Department at the University of Pennsylvania. Emily is a recipient of the Sloan Research Fellowship, a US Office of Naval Research Young Investigator award, and a National Science Foundation CAREER award. Her research interests are in large-scale Bayesian dynamic modeling and computations.

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By Manish S on 5-Jan-17

awesome clustering course

By GIRIJA N on 4-Nov-17

I really like the content of this course, like other courses in this specialization. However, for the assignment in module 5, one must work with GraphLab to get the correct answers in the purpose of getting a certificate. I think it is not very convenient for those who may have trouble accessing graph lab. I wonder if the instructors could provide a pandas/scikit learn version for assignment 2 in module 5. Thanks again for putting together such a great specialization.

By Mehul P on 16-Feb-18

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

By Ramavtar M on 25-Aug-16

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.

By Atsuya K on 6-Dec-16

Thank you, it was a good one

By RAVI P on 13-Aug-17

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!

By Samuele M on 27-Dec-16

great.

By MIAO K on 25-Jan-17

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.

By Marcos N F on 18-Sep-17