Machine Learning: Clustering & Retrieval
Provided by:

Provided by:

Course Details
Cost
FREE,
Add a Verified Certificate for $79
Upcoming Schedule
- Upcoming
Course Provider

Coursera online courses
Coursera's online classes are designed to help students achieve mastery over
course material. Some of the best professors in the world - like neurobiology
professor and author Peggy Mason from the University of Chicago, and computer
science professor and Folding@Home director Vijay Pande - will supplement your
knowledge through video lectures. They will also provide challenging
assessments, interactive exercises during each lesson, and the opportunity to
use a mobile app to keep up with yo...
Coursera's online classes are designed to help students achieve mastery over
course material. Some of the best professors in the world - like neurobiology
professor and author Peggy Mason from the University of Chicago, and computer
science professor and Folding@Home director Vijay Pande - will supplement your
knowledge through video lectures. They will also provide challenging
assessments, interactive exercises during each lesson, and the opportunity to
use a mobile app to keep up with your coursework. Coursera also partners with
the US State Department to create “learning hubs” around the world. Students
can get internet access, take courses, and participate in weekly in-person
study groups to make learning even more collaborative. Begin your journey into
the mysteries of the human brain by taking courses in neuroscience. Learn how
to navigate the data infrastructures that multinational corporations use when
you discover the world of data analysis. Follow one of Coursera’s “Skill
Tracks”. Or try any one of its more than 560 available courses to help you
achieve your academic and professional goals.
Provider Subject Specialization
Humanities
Sciences & Technology
Course Description
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 ...
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.
