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

1,801 個評分
307 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.


76 - Machine Learning: Clustering & Retrieval 的 100 個評論(共 295 個)

創建者 vivek k

May 25, 2017

awesome course!

創建者 Pankaj K

Sep 08, 2017

Great course, all the explanations are so good and well explained in the slides. Programming assignments are pretty challenging, but give really good insight into the algorithms!.


創建者 Veer A S

Mar 24, 2018

Very informative and interesting course.

創建者 shaonan

Nov 20, 2016

Deep insight into most useful techniques of machine learning.

創建者 Olga K

Sep 23, 2016

Excellent course! Subjects are explained very well! Excellent quizzes that allow understanding of lectures better and excellent (challenging ) programming assignments.

創建者 Li Y S

Oct 30, 2016

I really learn a lot in this course, although the materials are very difficult at first read, but Emily's explanation were clear and I would be able to get the idea after a few review.

創建者 suryatapa r

Dec 16, 2016

It's an amazing Course.

創建者 Anmol G

Dec 16, 2016

So Much Concepts to learn and totally worth it!

創建者 Saint-Clair d C L

Aug 30, 2016

This course has been an amazing experience. Congrats to you, Carlos and Emmy!

創建者 嵇昊雨

Apr 25, 2017


創建者 Jifu Z

Jul 23, 2016

Good class, But it would be much better if the quiz is open to those who doesn't pay.

創建者 Weituo H

Aug 29, 2016

strongly recommended!

創建者 Ridhwanul H

Oct 17, 2017

Like all the other ones, this as well was an amazing course. The topics covered in were the most interesting ones till now for me, as earlier days when I started programming I used often think about problems like these and used to wonder how it was done. Now I feel like I might be able to do them.

Its a shame that you no longer provide the Recommender System course, since that was something I was even more interested in, and its kinda sad that I am not gonna have access to it.

創建者 Kumiko K

Aug 14, 2016

This course started off easy, and became challenging in the last 3 weeks. But a lot of details were covered in the slides and also the forum helped deepen my understanding of the material, and I was able to get through the course. I enjoyed the course!

創建者 Moayyad A Y

Dec 04, 2016

this is not a an easy course but certainly an awesome one

創建者 Jinho L

Sep 20, 2016

Great! thanks

創建者 Pradeep N

Feb 22, 2017

"super one,

創建者 Kan C Y

Mar 19, 2017

Really a good course, succinct and concise.

創建者 Jaswant J

Mar 31, 2017

Very nice course. Concepts are covered very clearly.


Nov 12, 2017


創建者 Rohan K

Mar 22, 2018

Good introduction to very complicated concepts. I now have the tools to learn more about HHMs and anomaly detection.

創建者 MARC G

Oct 21, 2017

Clear and well designed course. The assignments are quite thorough. Sometimes, quiz question are not so clear though.

創建者 Roger S

Sep 04, 2016

Worth the wait. COOL learning

創建者 Yifei L

Jul 30, 2016

Good course for KD trees, LSH, Gaussian mixed model and LDA.

創建者 felix a f a

Aug 08, 2016

less complex exercises to check and validate