Chevron Left
返回到 Machine Learning: Clustering & Retrieval

學生對 华盛顿大学 提供的 Machine Learning: Clustering & Retrieval 的評價和反饋

4.6
2,128 個評分
368 條評論

課程概述

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....

熱門審閱

BK

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.

JM

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.

篩選依據:

301 - Machine Learning: Clustering & Retrieval 的 325 個評論(共 356 個)

創建者 Marco A d S M

Oct 20, 2017

As explicações poderiam ser um pouco mais detalhadas neste curto. Tive certa dificuldade em alguns conceitos apresentados, mais do que nos outros cursos.

創建者 Keith D

Jun 19, 2017

I'm disappointed that courses 5 and 6 of the specialization were cancelled. The cancelled capstone was why I purchased this specialization package.

創建者 Manish G

Jan 15, 2020

This topic was very deep and I learnt many complex algos. Would suggest to have some more examples for the algorithms presented in this modules.

創建者 Marcin W

Aug 09, 2016

Very good course. Too long interval between modules make hard for non-Python developers. Easy to forget some of the Python structures.

創建者 Farrukh N A

Mar 17, 2017

Great course on machine learning, however, left us in middle of learning, Recommender System + Deep Learning Capstone is missing

創建者 Iurii S

Nov 26, 2017

Good course overall.

Starting to get more on the side of being mostly implemented and only needing to insert a line or two.

創建者 Ayush K G

Feb 24, 2018

At some topics more explaination (eg. Map reduce and LDA) needed although as a whole it is good course.

創建者 Big O

Dec 21, 2018

More detail on theory behind LDA and HMMs would have been useful. Otherwise, another brilliant course!

創建者 Michael B

Sep 04, 2016

Good survey of the material, but assignments are superficial and don't test thorough understanding.

創建者 Kartoffel

Jul 26, 2016

Great course. Some week were tough others too easy, but general a very interesting course.

創建者 Hristo V

Aug 31, 2016

The last weeks, we went through the material a little bit too fast.

創建者 Iñaki D R

Sep 14, 2020

Excellent course with very detailed explanations and assignments

創建者 Andrey T

Aug 11, 2016

I did not understand LDA from the course materials.

創建者 charan S

Jul 30, 2017

Nice intuitive course with lots of understanding.

創建者 Jack B

Mar 04, 2017

Should use pandas instead of Graph Lab Create

創建者 Mehul P

Sep 11, 2017

Nice explanation on clustering methods.

創建者 Adwait B

Jan 26, 2018

Great Course! Tough topics well taught

創建者 ELINGUI P U

Aug 20, 2016

Great course like the others

創建者 Dony A

Jan 05, 2017

awesome clustering course

創建者 Galen S

May 08, 2017

I liked the slides.

創建者 Koen O

Aug 27, 2017

I liked it a lot

創建者 Dhanasekar S

Dec 24, 2016

I have enrolled myself in the other Machine Learning courses offered by Uwash , but have to say this was not properly organized. I had got my certificates for the other courses easily , not because the contents was easy , but was easily understandable and well organized and there was a great sense of satisfaction after getting the certificate because of the knowledge gained.But unfortunately for this course , especially the week 4 and week 5 was lengthy and not up to the point and the quizzes were hence not seem to be related. So got my certificate after a bit of struggle.

I'm planning to see other online materials related to week 4 and week 5 , as couldn't completely understand from this one. If you can modify those two weeks, it would be great. I hope you continue the great work of illuminating millions of young people's interests through your great courses and organization. Thank you from the bottom of my heart.

創建者 Diego T B

Aug 28, 2016

The retrieval part of this course is great, it deserve five starts. The clustering part was going well until it reached LDA.

The LDA module is very poorly covered, and also very hard to understand. I had to watch the videos more than two times to try to figure out what was LDA, and a Quora article posted in the Forum could explain it much better.

Then we get to the Hierarchical Clustering module, which was the most poorly module in all this specialization. There is only one video talking about HMM models, and Markov Chains deserve at least one week to even get started with it. And to complete, there is just one Assignment with only 3 questions.

The specialization was going perfect until now. I am very disappointed with this course. I hope the last two courses are much better covered and not just ran over like this this one was.

創建者 Sunil N

Jun 04, 2020

Emily and Carlos have done a wonderful job overall in stitching the specialization together. Bit disappointed by the shortening of the same by exclusive of the other two courses. Would have loved to do that having come forward to this extent. A minor feedback about the 4th course which I felt was that there was more reliance on verbal communication during lectures than on analogies or examples, making it tough to grasp certain concepts (or needing too much of focus on the verbiage). The assignments in the end and worked out examples were what turned out to be helpful at the end of the day, so kudos for providing them. I overall liked the journey and hopefully looking forward to implement the skills I have imbibed. Thank you and stay safe!

創建者 Ramesh S

Aug 22, 2018

The clustering course covered a lot of topics, and it seemed a bit hurried too. I felt the quizzes could have been better worded to make it less confusing. LDA in particular deserved a better treatment - more could have been done I thought in terms of explaining the mathematics as well as the intuition (relative to MoG). Overall, it was a good course, but the best way to judge this would have been to ask a question like this - "what if people did clustering and retrieval even before they did other modules (regression and classification) - would the faculty have dealt the subject in the same way? ". My guess, is "unlikely" and that kinda explains what was missing !