Retour à Machine Learning: Clustering & Retrieval

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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....

JM

16 janv. 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

24 août 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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par Pascal U E

•20 août 2016

Great course like the others

par Dony A

•5 janv. 2017

awesome clustering course

par Galen S

•8 mai 2017

I liked the slides.

par Koen O

•27 août 2017

I liked it a lot

par Dhanasekar S

•24 déc. 2016

I have enrolled myself in the other Machine Learning courses offered by Uwash , but have to say this was not properly organized. I had got my certificates for the other courses easily , not because the contents was easy , but was easily understandable and well organized and there was a great sense of satisfaction after getting the certificate because of the knowledge gained.But unfortunately for this course , especially the week 4 and week 5 was lengthy and not up to the point and the quizzes were hence not seem to be related. So got my certificate after a bit of struggle.

I'm planning to see other online materials related to week 4 and week 5 , as couldn't completely understand from this one. If you can modify those two weeks, it would be great. I hope you continue the great work of illuminating millions of young people's interests through your great courses and organization. Thank you from the bottom of my heart.

par Diego T B

•28 août 2016

The retrieval part of this course is great, it deserve five starts. The clustering part was going well until it reached LDA.

The LDA module is very poorly covered, and also very hard to understand. I had to watch the videos more than two times to try to figure out what was LDA, and a Quora article posted in the Forum could explain it much better.

Then we get to the Hierarchical Clustering module, which was the most poorly module in all this specialization. There is only one video talking about HMM models, and Markov Chains deserve at least one week to even get started with it. And to complete, there is just one Assignment with only 3 questions.

The specialization was going perfect until now. I am very disappointed with this course. I hope the last two courses are much better covered and not just ran over like this this one was.

par Sunil N

•4 juin 2020

Emily and Carlos have done a wonderful job overall in stitching the specialization together. Bit disappointed by the shortening of the same by exclusive of the other two courses. Would have loved to do that having come forward to this extent. A minor feedback about the 4th course which I felt was that there was more reliance on verbal communication during lectures than on analogies or examples, making it tough to grasp certain concepts (or needing too much of focus on the verbiage). The assignments in the end and worked out examples were what turned out to be helpful at the end of the day, so kudos for providing them. I overall liked the journey and hopefully looking forward to implement the skills I have imbibed. Thank you and stay safe!

par Ramesh S

•21 août 2018

The clustering course covered a lot of topics, and it seemed a bit hurried too. I felt the quizzes could have been better worded to make it less confusing. LDA in particular deserved a better treatment - more could have been done I thought in terms of explaining the mathematics as well as the intuition (relative to MoG). Overall, it was a good course, but the best way to judge this would have been to ask a question like this - "what if people did clustering and retrieval even before they did other modules (regression and classification) - would the faculty have dealt the subject in the same way? ". My guess, is "unlikely" and that kinda explains what was missing !

par Saeed S T

•7 sept. 2016

Overall a good and useful course, however:

A) They could do a much better job regarding LDA, standard Gibbs sampling, and Bayesian model and inference. Many slides on these 3 topics only contained some text and the instructor tried to "verbally" visualize the related important concepts. Hence not a good use of a video session.

B) Week 1 and the 1st half of Week 6 were redundant.

C) It would be much better to have a 7-week course with more topics and may be with some optional videos on Bayesian model, HMM.

par Adrien S

•7 oct. 2016

Feels like this course in the specialization was a bit rushed, compared to the first 3 courses. It had 2 modules (first & last) that were more like placeholders and the middle 4 modules went from concept to the maths behind the algorithm very quickly. It needs a bit of work on expanding the course and presenting a bit more slowly. Having said all that, the concepts and algorithms taught are very interesting and a good first step into the unsupervised learning section.

par Oliverio J S J

•20 juin 2018

Some of the contents of this course are interesting, but it seems that this course has been very affected by the changes that forced the cancellation of the last two courses of the specialization. Apparently, they had to redo it and there are even two fake weeks (the first one and the last one). It is a pity that they did not want to spend more time to reorganize it.

par Ahmed N

•17 juil. 2017

The course focus on a great part of researches i have never read about them or had any idea about all of it. It doesn't focus on how we implement the core functions of machine learning but it was all of benefits and very very good to me i have learned a lot of things thank you all it's very tough and challenging course for me thank you all.

par Dmitri B

•21 juin 2017

Theory is cool but programming assignments requires proficient phyton knowledge. GraphLab helps but it wont be used in real life in our company :(

I found strange that often optional topics are major part of quiz, but anyway you should watch everything :)

par Dimitris Z

•8 juin 2019

It has intresting theory but I believe the exercises need to be improvised. Maybe using Jupyter online and guiding the student to write code to solve the problems. In conclusion, I understood the basic theory but mostly that.

par Kayvan S

•15 févr. 2018

Great course but I think the workload could be spread across the weeks more. Also, I had a lot of trouble with the sklearn toolkit (probably due to installation issues.).

par Piotr Ś

•15 févr. 2017

Dependence on GraphLab technology is a big minus. The lectures are poorly balanced in terms of difficulty. Apart from that - interesting course, I'm glad I took it.

par Aayush G

•10 nov. 2016

This specific course traded off depth and detail for breadth of topics. Too many ideas were quickly described and not really built up to my liking.

par pavan b

•29 juil. 2019

Few concepts were covered in hurry with lot of concepts described abruptly. It took a while for me to do research about those topics to catchup.

par Alexander S

•7 août 2016

great course, but module 4 lacks a bit in structure. hard to follow. without the forum, it would not be possible to make it in time.

par J N B P

•16 oct. 2020

If you are familiar with the fundamental concepts of Clustering, unsupervised learning this course will help you move forward.

par Baubak G

•11 juil. 2018

Need more details in the coarse. I think many of the topics need more working on, and are not sufficiently described.

par Valentina S

•16 août 2016

Interesting content but explanations are less clear with respect to the other courses of the ML Specialization

par Michael L

•18 mars 2017

slightly repetitive of classification course with no real use-case value except lots of math..

par Rishabh s

•13 août 2020

explained with pretty much good effort but can be improved if they focus on coding as well

par Volker H

•18 juil. 2016

please rework in particular week 5, part 2

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