Chevron Left
Zurück zu Machine Learning: Clustering & Retrieval

Kursteilnehmer-Bewertung und -Feedback für Machine Learning: Clustering & Retrieval von University of Washington

1,756 Bewertungen
300 Bewertungen

Über den Kurs

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....



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.


Aug 25, 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.

Filtern nach:

126 - 150 von 289 Bewertungen für Machine Learning: Clustering & Retrieval

von Renato R R

Jan 05, 2018

von JOSE R

Nov 18, 2017

von Suresh K P

Dec 21, 2017

von Alessandro B

Dec 15, 2017

von Ruchi S

Jan 24, 2018

von Dongliang Z

Mar 22, 2018

von Phil B

Feb 13, 2018

von Victor C

Jun 24, 2017

von gaozhipeng

Dec 27, 2016

von Etienne V

Feb 19, 2017

von Sean L

Oct 04, 2016

von Jiancheng

Oct 27, 2016

von Patrick M

Aug 09, 2016

von Itrat R

Jan 23, 2017

von Yihong C

Sep 30, 2016

von vacous

Apr 18, 2018

von Freeze F

Oct 26, 2016

von Sally M

Jan 02, 2017

von Daniel W

Dec 23, 2016

von Kate S

Jun 30, 2017

von Mark h

Aug 08, 2017

von Kevin C N

Mar 26, 2017

von Job W

Jul 23, 2016

von Amey B

Dec 18, 2016

von 邓松

Jan 04, 2017