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Kursteilnehmer-Bewertung und -Feedback für Machine Learning: Clustering & Retrieval von University of Washington

1,717 Bewertungen
295 Bewertungen

Über den Kurs

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.

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126 - 150 von 284 Bewertungen für Machine Learning: Clustering & Retrieval

von 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.

von JOSE R

Nov 18, 2017

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

von Suresh K P

Dec 21, 2017

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

von Alessandro B

Dec 15, 2017

very useful and structured

von Ruchi S

Jan 24, 2018


von Dongliang Z

Mar 22, 2018

I enjoyed this course. This specialization is very good for machine learning beginner. Look forward to the next course anyway.

von 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.

von Victor C

Jun 24, 2017

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

von gaozhipeng

Dec 27, 2016


von Etienne V

Feb 19, 2017

Excellent course! Thanks a lot for the effort in compiling this course... I really enjoyed it!

von Sean L

Oct 04, 2016

wonderful course for beginner of machine learning.

von Jiancheng

Oct 27, 2016

Great intro!

von Patrick M

Aug 09, 2016

Excellent course. Nice selection of algorithms reviewed - all clearly explained with sample implementations.

von Itrat R

Jan 23, 2017

Excellent Course!!!

von Yihong C

Sep 30, 2016

a practical and interesting course about clustering and retrival

von 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.

von 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.

von Sally M

Jan 02, 2017

Great course but hard going at times for those of us without a strong maths background. The assignments took me a long time to complete and I think I'll have to revisit some areas as I become more familiar with them to really get the full benefit.

von Daniel W

Dec 23, 2016

Excellent course

von Kate S

Jun 30, 2017

I really enjoyed and learned a lot from this class. It made me interested to go out and learn other machine learning methods which are derived from what was taught.

von Mark h

Aug 08, 2017

Very helpful

von Kevin C N

Mar 26, 2017


von Job W

Jul 23, 2016


von Amey B

Dec 18, 2016

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

von 邓松

Jan 04, 2017

very helpful