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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.

4.5
287 reviews

Created by: Emily Fox

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Overall Score : 90 / 100

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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.
Read More product length iconTime: 47h

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Instructor Details

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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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Reviews

4.5

287 reviews in total

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By Prabhu on 2-Nov-19

Very clear explanation of concepts with a good selection of examples.

By Neemesh J on 28-Oct-19

Coursera is the best learning app. I am really thankful for getting very good training lectures.

By Jayant S on 25-Oct-19

The course was very detailed. The case study technique was rather very helpful as compared to theoretical technique. I would consider the programming assignments from medium to hard difficulty. The course could have been much better if graphlab as well as scikit coding would have been taught side by side.

By Muhammad W K on 22-Oct-19

A great course to get the grass-root level understanding of Clustering and Retrieval tasks and going beyond to Unsupervised learning and the core concepts related to it. And starting from the basics all the way to some of the advanced algorithms and models used in the world today. It is simply awesome!

By Parab N S on 13-Oct-19

Excellent course on clustering & retrieval by University of Washington

By Yao X on 30-Sep-19

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

By Manuel A on 8-Sep-19

Great course and specialization overall, both lectures and assignments

By Hanna L on 2-Sep-19

Great class!

By Muhammad Z H on 30-Aug-19

Machine Learning: Clustering & Retrieval, I have learned a lot professor

By leonardo d on 25-Aug-19

Awesome course. It was great to learn modern tools in machine learning, not just to apply some black-box on data. I also loved the applications that were showed: it is fantastic to see the algorithms in action, knowing how everything works inside. Another exiting ingredient was how the teachers show you the advantages and weaknesses of each method, as well as the suitable places were they can be applied, or even the most popular extensions or alternatives. I was really really great to had spent those months understanding machine learning in this course and during this favoluos entire specialization.

By Shuyi C on 19-Aug-19

I think it is easy to understand and good to practice. Nice entry level course!

By Banka C G on 10-Aug-19

Its my great experience for step by step modules

Showing 12 of 287 reviews

Machine Learning: Clustering & Retrieval
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  • Video Duration: icon 47h
  • Price: Free
  • Provider: icon Coursera
  • University: icon University of Washington
  • Language: icon English
  • Certificate: icon Yes

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