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

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


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.


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 76~100

교육 기관: Tripat S

2016년 8월 7일

This is the best course in ML - would recommend it ...the sequence of the courses is the best...the specialization in this ML is a career boost

교육 기관: sandeep d

2020년 8월 20일

excellent course by Emily and Carlos

I am glad to have this course

it contains clear view regarding clustering and its applications from roots

교육 기관: Shaowei P

2016년 8월 8일

very good course but the last few topics could be improved with better assignments that could be broken down into smaller sub assignments

교육 기관: Jared C

2016년 8월 7일

Exceptional course! This is challenging material for me, but it's presented in such a coherent manner that you can't help but absorb it.

교육 기관: Saqib N S

2016년 12월 4일

The course dived into basic and advanced concepts of unsupervised learning. As before, Prof Fox did a great job at explaining things.

교육 기관: Yao X

2019년 9월 29일

Wish to have more detail on implementing the algorithm. Assignments are too easy for understanding the knowledge behind the scene.

교육 기관: Songxiang L

2016년 12월 4일

Very good, not only learn many good ML concepts, but also polish my python programming skill a lot. Thank you, Emily and Carlos.

교육 기관: Dongliang Z

2018년 3월 22일

I enjoyed this course. This specialization is very good for machine learning beginner. Look forward to the next course anyway.

교육 기관: Целых А Н

2020년 6월 7일

Find the course useful. The authors presented a simple and clear visualization of the meaning of algorithms. Excellent!


교육 기관: Robert C

2018년 2월 16일

Emily was fantastic at explaining difficult to understand concepts. Thoroughly enjoyed the course, and learned quite a lot.

교육 기관: Kuntal G

2016년 11월 3일

Very Good in depth explanation and hand-on lab machine learning course. very focused on real world analytics and algorithms

교육 기관: Arun K P

2018년 10월 27일

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

교육 기관: Jose J M T

2017년 4월 14일

The teachers are really amazing. They do not just explain it as if they read a book. They explain the concepts very well

교육 기관: Vikash S N

2019년 2월 3일

It was great but I was also interested to implement the solutions with pyspark...though I did it eventually. Thank you!

교육 기관: Marc G

2017년 10월 21일

Clear and well designed course. The assignments are quite thorough. Sometimes, quiz question are not so clear though.

교육 기관: Andrey N

2017년 3월 12일

Some themes are shown very superficially it would be great to go deeper. Despite of this the course is great!


교육 기관: Rohan K

2018년 3월 22일

Good introduction to very complicated concepts. I now have the tools to learn more about HHMs and anomaly detection.

교육 기관: Justin K

2016년 8월 17일

An interesting topic, presented well by the instructor and reinforced by intermediate-level programming assignments.

교육 기관: Somu P

2018년 11월 17일

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

교육 기관: Édney M V F

2022년 1월 10일

Diferente dos demais cursos esse é muito mais direto, depende diretamente de você ter feito os cursos anteriores.

교육 기관: Freeze F

2016년 10월 26일

From LDA onwards the pace ramped up ! Please be slow during advance topics. But altogether it was a great course.

교육 기관: Fahad S

2018년 11월 3일

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

교육 기관: Patrick M

2016년 8월 8일

Excellent course. Nice selection of algorithms reviewed - all clearly explained with sample implementations.

교육 기관: Jorge L

2017년 5월 26일

I'm a grad student and I can notice the instructor makes a difference in this course. I fully recommend it.


2017년 5월 7일

very good! strongly recommend to people who want to start a career on data science or are interested in it