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Machine Learning: Clustering & Retrieval(으)로 돌아가기

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

4.6
별점
2,299개의 평가
393개의 리뷰

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

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.

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.

필터링 기준:

Machine Learning: Clustering & Retrieval의 381개 리뷰 중 376~381

교육 기관: SHAHAPURKAR S M

2020년 6월 19일

Course content is good but assignments are too lengthy and directions are not clear. Also, no support has been provided for non TuriCreate users. Students face a hard time in figuring out the Scikit-Learn implementations of the functions provided in the notebooks.

교육 기관: Karl S

2016년 10월 11일

For me, this course was disappointing. Here is why: First, the level, at which the course material is presented, is very low. It might be freshman level, but certainly not more. There are many buzzwords but no real explanations. The programming assignments are only doable because most of the work has been done by the people designing the assignments. There is very little left for the students. Furthermore, the procedures, that are already given, are not very well documented. Hence, a lot of guess work is required to figure out how things should work. Furthermore, little effort has been spent to structure the procedures that are already given. Altogether, this makes doing the programming assignments very unsatisfying.

Finally, the professor presenting the materials does not take part in the discussion forums. Contrary to other courses that I have attended at Coursera, this time the discussion forum was no help at all.

교육 기관: Ricardo Y N

2020년 8월 17일

some exercises only works if you have a Linux or MacOS, you could not resolve them if you have windows, the explanations are ok, I've never had an anwswer for my questions or issues on hte forum

교육 기관: Kripakaran R

2018년 11월 12일

I wish week4 and week5 were better. It felt so rushed, where most of the important things were covered.

교육 기관: Andreas

2017년 1월 4일

This specialization is delayed for months now - very annoying! Don't give them money!

교육 기관: Adrien L

2017년 2월 2일

No good without the missing course and capstone projects