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

1,785 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....



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.


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.


201 - Machine Learning: Clustering & Retrieval 的 225 個評論(共 293 個)

創建者 kripa s

Apr 30, 2019

One of the best training experience...

創建者 Mohamed A H

Jun 20, 2019

A very rich of useful materials course. The instructor has a fantastic explanation ability. The course is pretty organized and the assignments solidifies the understanding of the concepts well.

It was an amazing experience!

創建者 Aakash S

Jun 19, 2019

Such a clear explanation of topics of clustering. Without doubt one of the best in business.

創建者 Jafed E

Jul 06, 2019

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

創建者 Yufeng X

Jul 09, 2019

It opened the door to more advanced techniques.

創建者 Dennis S

May 19, 2019

Amazing course. The Instructors did an awesome job of preparing and presenting the material.

I think there is no better and more approachable in-depth course out there. Thank you so much!


May 31, 2019

Awesome course to understand the concept behind Gaussian Mixture model.

創建者 Dohyoung C

Jun 04, 2019

Fascinating course…

LDA is little bit difficult to understand, but K-mean and Mixture models are easy to understand and quite important for clustering..

創建者 Banka C G

Aug 10, 2019

Its my great experience for step by step modules

創建者 Shuyi C

Aug 19, 2019

I think it is easy to understand and good to practice. Nice entry level course!

創建者 leonardo d

Aug 25, 2019

Awesome course. It was great to learn modern tools in machine learning, not just to apply some black-box on data. I also loved the applications that were showed: it is fantastic to see the algorithms in action, knowing how everything works inside. Another exiting ingredient was how the teachers show you the advantages and weaknesses of each method, as well as the suitable places were they can be applied, or even the most popular extensions or alternatives. I was really really great to had spent those months understanding machine learning in this course and during this favoluos entire specialization.

創建者 Muhammad Z H

Aug 30, 2019

Machine Learning: Clustering & Retrieval, I have learned a lot professor

創建者 Hanna L

Sep 02, 2019

Great class!

創建者 Prabhu

Nov 02, 2019

Very clear explanation of concepts with a good selection of examples.

創建者 Yao X

Sep 30, 2019

Wish to have more detail on implementing the algorithm. Assignments are too easy for understanding the knowledge behind the scene.

創建者 Manuel A

Sep 08, 2019

Great course and specialization overall, both lectures and assignments

創建者 Parab N S

Oct 13, 2019

Excellent course on clustering & retrieval by University of Washington

創建者 Muhammad W K

Oct 22, 2019

A great course to get the grass-root level understanding of Clustering and Retrieval tasks and going beyond to Unsupervised learning and the core concepts related to it. And starting from the basics all the way to some of the advanced algorithms and models used in the world today. It is simply awesome!

創建者 Neemesh J

Oct 28, 2019

Coursera is the best learning app. I am really thankful for getting very good training lectures.

創建者 Srinivas C

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.

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 Martin B

Apr 11, 2019

Greatly enjoyed it. As with the other courses in this specialization the discussion of the subjects is impeccable, especially if you've taken some preparatory mathematics courses. The reliance on Graphlab Create is a drag though.


Aug 20, 2016

Great course like the others

創建者 Yin X

Nov 04, 2017

I really like the content of this course, like other courses in this specialization. However, for the assignment in module 5, one must work with GraphLab to get the correct answers in the purpose of getting a certificate. I think it is not very convenient for those who may have trouble accessing graph lab. I wonder if the instructors could provide a pandas/scikit learn version for assignment 2 in module 5. Thanks again for putting together such a great specialization.

創建者 Dony A

Jan 05, 2017

awesome clustering course