Chevron Left
返回到 Machine Learning: Clustering & Retrieval

Machine Learning: Clustering & Retrieval, 华盛顿大学

4.6
1,576 个评分
277 个审阅

课程信息

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....

热门审阅

创建者 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.

创建者 AG

Sep 25, 2017

Nice course with all the practical stuffs and nice analysis about each topic but practical part of LDA was restricted for GraphLab users only which is a weak fallback and rest everything is fine.

筛选依据:

265 个审阅

创建者 Edwin Augusto Pucuji Jácome

Feb 15, 2019

Excellent, good contribution to the technical and practical knowledge ML

创建者 Zhongkai Mi

Feb 12, 2019

Great assignments : )

创建者 Vikash Singh Negi

Feb 03, 2019

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

创建者 Srinivas CS

Jan 07, 2019

This was a really good course, It made me familiar with many tools and techniques used in ML. With this in hand I will be able to go out there and explore and understand things much better.

创建者 Jay Kumar Sinha

Jan 05, 2019

Excellent course material and fantastic delivery. You guys made this complex learning so simple and interesting . Thanks for all this, keep the good works.

创建者 KAI NIU

Jan 03, 2019

Excellent course with great and reachable explanation

创建者 PRAVEEN REDDY UPPALA

Dec 27, 2018

Nice content and well made presentations.

创建者 Big O

Dec 21, 2018

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

创建者 Xue

Dec 19, 2018

Great but hard~!

创建者 Martin Rehfeld

Dec 12, 2018

I'd bring the last summary video at the beginning (the great summary of all weeks of the course). This would outline the course evolution in advance and give guidance what's ahead. IMHO this would help to not get lost when drill down in a single section.