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

4.6
1,777 個評分
305 個審閱

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.

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126 - Machine Learning: Clustering & Retrieval 的 150 個評論(共 293 個)

創建者 Lalithmohan S

Mar 26, 2018

V

創建者 李紹弘

Aug 22, 2017

This course provides concise course.

創建者 Yong D K

May 07, 2018

This is the best course for Information Retrieval ever!

創建者 Ayan M

Dec 04, 2016

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

創建者 zhenyue z

Aug 09, 2016

very nice lecture

創建者 Krisda L

Jul 19, 2017

Good overview of a lot of useful techniques.

創建者 Suresh K P

Dec 21, 2017

Interesting, lot of Algorithms and methods to use iin upcoming projects and real time applications

創建者 Mark W

Aug 12, 2017

Excellent course. Emily and Carlos are fantastic teachers and have clearly put in a huge amount of effort in makign a great course. Thanks guys!

創建者 Pankaj K J

Oct 28, 2017

A great course to understand clustering as well as text mining. Lectures on KDD and LSH are equally important to understand and implement these algo . Many thanks

創建者 Cuiqing L

Nov 05, 2016

great!

創建者 Nguyen D P

Feb 08, 2018

This is very useful course that can help me more to understand and resolve the complicated issue in the real world. I want to thank Coursera e-learning and the Washinton University for created this course that help people in the developing country like me can access the new technical.

創建者 Foo C S G

Mar 04, 2018

Tough slog, but well designed

創建者 Yang X

Nov 15, 2017

Thank you Emily and Carlos! You guys are amazing!!!

創建者 IDOWU H A

May 20, 2018

Excellent - Goo

創建者 Sean S

Apr 03, 2018

Another great course and sadly the last of this specialization. I found the material for this course to be the most challenging yet, specifically the LDA module. The programming assignments were all very manageable thanks to graphlab and the very explicit hints provided but I do not feel like I reached the same level of understanding as I did for the previous courses in the specialization. I have grown to enjoy using graphlab and would likely use it going forward if not for the licensing. I am very disappointed that the remaining courses will not be offered and am now in search for another great machine learning resource.

創建者 Alvaro M M

Jan 07, 2018

I liked it a lot. My only problem was to get the GraphLab to work here. Loved the option to download the videos and material before and the content is awesome.

創建者 Suneel M

May 09, 2018

E

創建者 Renato R R

Jan 05, 2018

This course is amazing. I could really work on real world problems. It is a pity that we are not going to have the following courses:

Recommender Systems & Dimensionality Reduction

Machine Learning Capstone: An Intelligent Application with Deep Learning

Thank you Emily and Carlos.

創建者 JOSE R

Nov 18, 2017

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

創建者 Swati D

May 02, 2018

This course is a very structured and progressive learning. It is an advantage , if we know python . However, one can still manage and explore Machine learning and Deep learning concept of AI. The case study and real life approach keeps your quest on. This is a great initiative and gives us an opportunity to be future ready while at job. Many notion went wrong about AI and the chapters are well designed to keep us engaged while we

創建者 Shuang D

Jun 29, 2018

advanced knowledge on ML, great course

創建者 Brian N

May 20, 2018

This course is exciting

創建者 Alessandro B

Dec 15, 2017

very useful and structured

創建者 Ruchi S

Jan 24, 2018

E

創建者 Phil B

Feb 13, 2018

Again the lecturing style and course content were excellent, allowing us to write fairly complex functions to implement our own algorithms from scratch but also using pre-built functions when necessary to allow us to explore the effects of different variables. The benefits and costs of the different types of clustering were clearly stated. It's a shame that the specialization stops here, as a capstone project with the same quality of these 4 courses would really provide the students with something they can show off to potential employers. The problem most students will have when coming off this specialization is how to implement and deploy your own model into a service like a website.