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

1,782 個評分
305 個審閱


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....



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.


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.


151 - Machine Learning: Clustering & Retrieval 的 175 個評論(共 293 個)

創建者 Victor C

Jun 24, 2017

Excellent teacher and material. I wish there were more...

創建者 gaozhipeng

Dec 27, 2016


創建者 Yihong C

Sep 30, 2016

a practical and interesting course about clustering and retrival

創建者 vacous

Apr 18, 2018

Very good content, and great practices. Coding a algorithm from the scratch definitely helped my understanding. The more challenging knowledge like LDA and HMM in the last two weeks are not covered well in great details, but I can understand the course design since that the foundation knowledge required to understand of those algorithms are much more advanced than the previous ones.

Overall, I enjoy this course and the specilization overall, except the Graphlab part which is very confusing and rarely used in the industry.

創建者 Freeze F

Oct 26, 2016

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

創建者 Siwei Y

Jan 17, 2017

本来不报什么期望,但是该门课确实做得相当好。 相信该课的老师们花了巨大的心血。真的是业界良心。所以强烈点赞。

創建者 Gillian P

Jul 23, 2017

A very good course with two engaging and sympathetic teachers. Would love to see the next courses

創建者 roi s

Oct 29, 2017

Great, very hands on!

創建者 Ferenc F P

Jan 25, 2018

Very good course. Even though I had some machine learning background, this course provided new insights and new algorithms, like KDTree, Locally Sensitive Hashing, Latent Dirichlet Allocation, and mixture of Gaussians. the only drawback is that with scikitlearn, not always you get the same results as with GraphLab. Thanks for the instructors for this great specialization.

創建者 Garvish

Jun 14, 2017

Great Information and organised course

創建者 Lalithmohan S

Mar 26, 2018


創建者 Job W

Jul 23, 2016


創建者 Amey B

Dec 18, 2016

Very Insightful. Great Instructors. Awesome Forum and intelligible peers.


May 07, 2017

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

創建者 José M T

Apr 14, 2017

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

創建者 邓松

Jan 04, 2017

very helpful

創建者 Sameer M

Sep 19, 2017

Excellent course! must for machine learning beginners!!

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 Kevin C N

Mar 26, 2017


創建者 David F

Oct 21, 2016

Excellent course - and of great practical use.

創建者 Brandon H

Dec 14, 2016

This was probably the most challenging course of them all, I thoroughly enjoyed it! Looking forward to dimensionality reduction and the capstone.