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Bewertung und Feedback des Lernenden für Machine Learning: Clustering & Retrieval von University of Washington

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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

16. Jan. 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. Aug. 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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376 - 383 von 383 Bewertungen für Machine Learning: Clustering & Retrieval

von Pan W

3. Jan. 2017

I give 5 star for the teacher, really approach having such a well-organized teaching material.

I also give -1 star for the homework assignment and its (almost) GraphLab only approach. Yes, it mentioned "alternative" approach (which is much more popular than GraphLab), but there are many bugs & trivial difficulties to get it through. With scikit-learn as a great open source package, the only reason (I suspect) to choose GraphLab is commercial purpose. For me, if the homework assignment is only instructed properly for loading data into Pandas, I can finish each programming assignment within 1 hour for sure using scikit learn; but now, it takes 30 minutes and I still cannot load the data correctly. I like to get a certificate, but it is not necessary and spending too much time is a waste on my time.

von ryan

23. Sep. 2017

requires use of a programming library from a company that was sold and is unmaintained. Challenging to build the environment to run the homework code on my mac pro. An AMI is provided so you can try to do the assignments on a prebuilt machine. Anyway I've found the class quite a hassle.

von SHAHAPURKAR S M

19. Juni 2020

Course content is good but assignments are too lengthy and directions are not clear. Also, no support has been provided for non TuriCreate users. Students face a hard time in figuring out the Scikit-Learn implementations of the functions provided in the notebooks.

von Karl S

11. Okt. 2016

For me, this course was disappointing. Here is why: First, the level, at which the course material is presented, is very low. It might be freshman level, but certainly not more. There are many buzzwords but no real explanations. The programming assignments are only doable because most of the work has been done by the people designing the assignments. There is very little left for the students. Furthermore, the procedures, that are already given, are not very well documented. Hence, a lot of guess work is required to figure out how things should work. Furthermore, little effort has been spent to structure the procedures that are already given. Altogether, this makes doing the programming assignments very unsatisfying.

Finally, the professor presenting the materials does not take part in the discussion forums. Contrary to other courses that I have attended at Coursera, this time the discussion forum was no help at all.

von Ricardo Y N

17. Aug. 2020

some exercises only works if you have a Linux or MacOS, you could not resolve them if you have windows, the explanations are ok, I've never had an anwswer for my questions or issues on hte forum

von Kripakaran R

12. Nov. 2018

I wish week4 and week5 were better. It felt so rushed, where most of the important things were covered.

von Andreas

4. Jan. 2017

This specialization is delayed for months now - very annoying! Don't give them money!

von Adrien L

2. Feb. 2017

No good without the missing course and capstone projects