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워싱턴 대학교의 Machine Learning: Clustering & Retrieval 학습자 리뷰 및 피드백

4.6
별점
2,299개의 평가

강좌 소개

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....

최상위 리뷰

JM

2017년 1월 16일

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.

BK

2016년 8월 24일

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.

필터링 기준:

Machine Learning: Clustering & Retrieval의 381개 리뷰 중 126~150

교육 기관: Robi s

2017년 9월 17일

Great instruction, great course, and provide information I used directly in my work.

교육 기관: Russell H

2016년 10월 9일

Detailed coverage of several approaches to clustering. Not easy but learned a lot.

교육 기관: Manuel S

2016년 10월 1일

Amazing course, really helpful, as a ML researcher you need this kind of foundation

교육 기관: Shuyi C

2019년 8월 19일

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

교육 기관: Anshumaan K P

2020년 11월 11일

Good Specialization. But some assignments make it more cool i.e, not here :)

교육 기관: Saint-Clair d C L

2016년 8월 30일

This course has been an amazing experience. Congrats to you, Carlos and Emmy!

교육 기관: Thanos K

2021년 1월 7일

This is an exceptional and challenging specialization. So much to take away

교육 기관: Ayan M

2016년 12월 4일

Excellent! Very good material and lectures and hands on. Really enriching.

교육 기관: kamez 0

2016년 12월 18일

Very Insightful. Great Instructors. Awesome Forum and intelligible peers.

교육 기관: Muhammad Z H

2019년 8월 30일

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

교육 기관: YASHKUMAR R T

2019년 5월 31일

Awesome course to understand the concept behind Gaussian Mixture model.

교육 기관: Edwin P

2019년 2월 15일

Excellent, good contribution to the technical and practical knowledge ML

교육 기관: Parab N S

2019년 10월 12일

Excellent course on clustering & retrieval by University of Washington

교육 기관: Manuel A

2019년 9월 8일

Great course and specialization overall, both lectures and assignments

교육 기관: Prabhu D

2019년 11월 2일

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

교육 기관: Hans H

2018년 7월 27일

Amazing course, I´ve learned so much stuff that I can use in my job.

교육 기관: Swapnil A

2020년 9월 6일

Really awesome course. Dr. Emily explains everything from scratch.

교육 기관: Jonathan H

2017년 7월 1일

Emily is great! Excellent course that covers a ton of material!!!

교육 기관: johny a v o

2020년 11월 21일

very helpfull the course, congrat!!! and thank u for this course

교육 기관: Yihong C

2016년 9월 30일

a practical and interesting course about clustering and retrival

교육 기관: Ben L

2017년 6월 10일

The most challenging of the four courses in the specialization.

교육 기관: Eric N

2020년 10월 11일

Excellent online teaching with clear and concise explanations!

교육 기관: Akash G

2019년 3월 11일

Machine Learning: Clustering & Retrieval good and learn easily

교육 기관: Shaonan W

2016년 11월 20일

Deep insight into most useful techniques of machine learning.

교육 기관: JOSE R

2017년 11월 18일

Very well explained. The LDA was difficult to learn. Thanks.