Chevron Left
返回到 Machine Learning: Clustering & Retrieval

学生对 华盛顿大学 提供的 Machine Learning: Clustering & Retrieval 的评价和反馈

2,307 个评分


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 24, 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 16, 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.


76 - Machine Learning: Clustering & Retrieval 的 100 个评论(共 381 个)

创建者 Tripat S

Aug 7, 2016

创建者 sandeep d

Aug 20, 2020

创建者 Shaowei P

Aug 8, 2016

创建者 Jared C

Aug 7, 2016

创建者 Saqib N S

Dec 4, 2016

创建者 Yao X

Sep 29, 2019

创建者 Songxiang L

Dec 4, 2016

创建者 Dongliang Z

Mar 22, 2018

创建者 Целых А Н

Jun 7, 2020

创建者 Robert C

Feb 16, 2018

创建者 Kuntal G

Nov 3, 2016

创建者 Arun K P

Oct 27, 2018

创建者 Jose J M T

Apr 14, 2017

创建者 Vikash S N

Feb 3, 2019

创建者 Marc G

Oct 21, 2017

创建者 Andrey N

Mar 12, 2017

创建者 Rohan K

Mar 22, 2018

创建者 Justin K

Aug 17, 2016

创建者 Somu P

Nov 17, 2018

创建者 Édney M V F

Jan 10, 2022

创建者 Freeze F

Oct 26, 2016

创建者 Fahad S

Nov 3, 2018

创建者 Patrick M

Aug 8, 2016

创建者 Jorge L

May 26, 2017


May 7, 2017