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

4.6
1,756 Bewertungen
300 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....

Top-Bewertungen

JM

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.

BK

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.

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201 - 225 von 289 Bewertungen für Machine Learning: Clustering & Retrieval

von kripa s

Apr 30, 2019

One of the best training experience...

von 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!

von YASHKUMAR R T

May 31, 2019

Awesome course to understand the concept behind Gaussian Mixture model.

von 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..

von Aakash S

Jun 19, 2019

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

von 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!

von 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

von Yufeng X

Jul 09, 2019

It opened the door to more advanced techniques.

von Banka C G

Aug 10, 2019

Its my great experience for step by step modules

von Manuel A

Sep 08, 2019

Great course and specialization overall, both lectures and assignments

von Shuyi C

Aug 19, 2019

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

von 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.

von Muhammad Z H

Aug 30, 2019

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

von Hanna L

Sep 02, 2019

Great class!

von 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.

von Parab N S

Oct 13, 2019

Excellent course on clustering & retrieval by University of Washington

von Big O

Dec 21, 2018

More detail on theory behind LDA and HMMs would have been useful. Otherwise, another brilliant course!

von 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.

von Sundar J D

Sep 26, 2016

Great course and awesome teaching by Prof. Emily Fox. Prof. Fox did a great job of teaching some of the really tough components (GMM, LDA, etc) in simple and lucid style (like always) and that made it easy to understand and comprehend those topics.

The one thing that I felt had gone down compared to the previous 3 courses was that for some of the topics, the material felt too short and felt like it was cut down to fit within the 6 weeks course duration. I would have at least liked some extra reading material or references especially for GMMs, LDA, Gibbs Sampling, etc.

von Adwait B

Jan 26, 2018

Great Course! Tough topics well taught

von Steve S

Aug 26, 2016

Like all the courses in this specialization so far, the material has been good. The reason for only 4 stars rather than 5 is the difficulty in getting questions answered in a timely manner. There don't seem to be any active mentors for this class.

von Sander v d O

Oct 18, 2016

All the courses in this specialization are great, but compared to the other 3 i did until now, this one seemed a bit short on material. Especially week 1, and somewhat week 6 was without good material. Weeks 2, 3 and 4 were great. I got lost somewhere in week 5 on collapsed Gibbs sampling.

Still: very much recommend this course, it provides a good introduction to Nearest Neighors, K-Means, Gaussian Mixtures and LDA. Thx prof. Fox!!

von Pier L L

Aug 02, 2016

Very good course nice practical approach. I was kind of surprised that hierarchical clustering was kept at the end and discussed only marginally since it is a widely used approach.

I liked the part about LDA but IMHO I would have liked more discussion about fundamental techniques rather than such an advanced method.

Too focus on text data. Most of the application I worked on have limited textual data.

von Keith D

Jun 19, 2017

I'm disappointed that courses 5 and 6 of the specialization were cancelled. The cancelled capstone was why I purchased this specialization package.

von Andrey C

Apr 10, 2017

Overall is great. The LDA and Dendrograms lack quality/specificity and depth of the previous topics. So sad the Specialization collapsed at 4 courses instead of 6.