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Avis et commentaires pour d'étudiants pour Machine Learning: Clustering & Retrieval par Université de Washington

2,299 évaluations
393 avis

À propos du cours

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....

Meilleurs avis


24 août 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.


16 janv. 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.

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126 - 150 sur 381 Avis pour Machine Learning: Clustering & Retrieval

par Robi s

17 sept. 2017

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

par Russell H

9 oct. 2016

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

par Manuel S

1 oct. 2016

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

par Shuyi C

19 août 2019

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

par Anshumaan K P

11 nov. 2020

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

par Saint-Clair d C L

30 août 2016

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

par Thanos K

7 janv. 2021

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

par Ayan M

4 déc. 2016

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

par kamez 0

18 déc. 2016

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

par Muhammad Z H

30 août 2019

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


31 mai 2019

Awesome course to understand the concept behind Gaussian Mixture model.

par Edwin P

15 févr. 2019

Excellent, good contribution to the technical and practical knowledge ML

par Parab N S

12 oct. 2019

Excellent course on clustering & retrieval by University of Washington

par Manuel A

8 sept. 2019

Great course and specialization overall, both lectures and assignments

par Prabhu D

2 nov. 2019

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

par Hans H

27 juil. 2018

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

par Swapnil A

6 sept. 2020

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

par Jonathan H

1 juil. 2017

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

par johny a v o

21 nov. 2020

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

par Yihong C

30 sept. 2016

a practical and interesting course about clustering and retrival

par Ben L

10 juin 2017

The most challenging of the four courses in the specialization.

par Eric N

11 oct. 2020

Excellent online teaching with clear and concise explanations!

par Akash G

11 mars 2019

Machine Learning: Clustering & Retrieval good and learn easily

par Shaonan W

20 nov. 2016

Deep insight into most useful techniques of machine learning.

par JOSE R

18 nov. 2017

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