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

1,762 Bewertungen
301 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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76 - 100 von 289 Bewertungen für Machine Learning: Clustering & Retrieval

von Jason G

Aug 09, 2017

Harder than the previous ones, but enjoyable

von vivek k

May 25, 2017

awesome course!

von 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!.


von Veer A S

Mar 24, 2018

Very informative and interesting course.

von shaonan

Nov 20, 2016

Deep insight into most useful techniques of machine learning.

von 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.

von 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.

von suryatapa r

Dec 16, 2016

It's an amazing Course.

von Anmol G

Dec 16, 2016

So Much Concepts to learn and totally worth it!

von Saint-Clair d C L

Aug 30, 2016

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

von 嵇昊雨

Apr 25, 2017


von Jifu Z

Jul 23, 2016

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

von Kim K L

Oct 04, 2016

Another super course. Though admittedly (for me at least) very difficult to make within the allotted time given for one period of the Course. Lots of advanced stuff that require substantial studies to really comprehend, i.e., it should never be enough just to hack & run the code (that's the easier challenge). Still have a long washing list of topics coming out of this Course that I need (want) to understand better. But at least the background to do so is neatly provided here. So without further ado ... Applause!

von Weituo H

Aug 29, 2016

strongly recommended!

von 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.

von 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!

von Ce J

Jun 26, 2017

well organized and easy to understand

von Moayyad A Y

Dec 04, 2016

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

von Jinho L

Sep 20, 2016

Great! thanks

von Pradeep N

Feb 22, 2017

"super one,

von Alessio D M

Aug 01, 2016

Very nice course, and a great grasp on clustering techniques. If I could just suggest something to improve, it would be the section on LDA and Gibbs: it's very high level and it would be really nice to have some more technical insights on those techniques (perhaps with optional sections, as for other topics).

von Kan C Y

Mar 19, 2017

Really a good course, succinct and concise.

von Yi W

Sep 28, 2016

As someone very keen on math, more math background as optimal video would be more helpful.

von Karundeep Y

Sep 18, 2016

Best Course.

von Phan T B

Aug 08, 2016

very good course