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Machine Learning: Clustering & Retrieval, University of Washington

4.6
1,502 个评分
267 个审阅

课程信息

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....

热门审阅

创建者 JM

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.

创建者 AG

Sep 25, 2017

Nice course with all the practical stuffs and nice analysis about each topic but practical part of LDA was restricted for GraphLab users only which is a weak fallback and rest everything is fine.

筛选依据:

255 个审阅

创建者 Manoj Kumar

Nov 26, 2018

session was very helpful & full with relevant contents

创建者 Somu Patil

Nov 17, 2018

Excellent course, which gives you all you need to learn about machine learning. Concepts and hands on practical ex

创建者 Juan Fernando Hernández

Nov 15, 2018

The teacher is awesome

创建者 Susree Sangita Mohanty

Nov 14, 2018

This course is very useful to know about the concepts of machine learning and do hands-on activities.

创建者 Kripakaran Ravivarman

Nov 12, 2018

I wish week4 and week5 were better. It felt so rushed, where most of the important things were covered.

创建者 VITTE

Nov 11, 2018

Excellent.

创建者 Nagendra Kumar M R

Nov 11, 2018

G

创建者 Fahad Sarfraz

Nov 03, 2018

Emily ross is an amazing instructor. The course introduces many complex topics and presents them intuitively.

创建者 Yugandhar Devarapalli

Oct 29, 2018

Excellent course on clustering and retreival. The assignments were thorough and productive.

创建者 Arun Kumar Pradhan

Oct 27, 2018

Very useful and informative .It help and provide confidence to the job more effectively. Thanks for the help and good cour