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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....

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.

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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von Kartoffel

•Jul 26, 2016

Great course. Some week were tough others too easy, but general a very interesting course.

von Hristo V

•Aug 31, 2016

The last weeks, we went through the material a little bit too fast.

von Andrey T

•Aug 11, 2016

I did not understand LDA from the course materials.

von charan S

•Jul 30, 2017

Nice intuitive course with lots of understanding.

von Jack B

•Mar 04, 2017

Should use pandas instead of Graph Lab Create

von Mehul P

•Sep 11, 2017

Nice explanation on clustering methods.

von Adwait B

•Jan 26, 2018

Great Course! Tough topics well taught

von ELINGUI P U

•Aug 20, 2016

Great course like the others

von Dony A

•Jan 05, 2017

awesome clustering course

von Galen S

•May 08, 2017

I liked the slides.

von Koen O

•Aug 27, 2017

I liked it a lot

von Dhanasekar S

•Dec 24, 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.

von Diego T B

•Aug 28, 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.

von Ramesh S

•Aug 22, 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 !

von Saeed S T

•Sep 08, 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.

von Adrien S

•Oct 07, 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.

von Oliverio J S J

•Jun 20, 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.

von Ahmed N

•Jul 18, 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.

von Dmitri B

•Jun 21, 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 :)

von Dimitrios Z

•Jun 08, 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.

von Kayvan S

•Feb 15, 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.).

von Piotr Ś

•Feb 15, 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.

von Aayush G

•Nov 10, 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.

von Pavan B

•Jul 29, 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.

von Alexander S

•Aug 07, 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.

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