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

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2,244 Bewertungen
383 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

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.

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.

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301 - 325 von 371 Bewertungen für Machine Learning: Clustering & Retrieval

von Nicolas S

2. Jan. 2020

The videos are great, well-structured and introduce gradually the complexity. This is a good idea to explore both methodological and computation aspects of clustering. Unfortunately, the exercises requires the use of a specific library, instead of scikit-learn and numpy. Furthermore, they also required Python 2, while Python 3 is now widely used.

von Gilles D

12. Aug. 2016

Still a very good course.

Week 4 was very tough. The general concept can be understood from a 10,000 feet altitude but the lesson and programming assignment need to be reviewed, maybe with a slower step by step example.

As some other student mentioned, it was... "brutal".

Other than that looking forward to the next course in the specialization!

von Jayant S

25. Okt. 2019

The course was very detailed. The case study technique was rather very helpful as compared to theoretical technique. I would consider the programming assignments from medium to hard difficulty. The course could have been much better if graphlab as well as scikit coding would have been taught side by side.

von Patrick A

30. Sep. 2020

Very interesting but the LDA and Gibbs sampling for LDA concepts were not easy to understand. May be we could find a simpler way to explain them. Nonetheless, with all these concepts and practical case studies learned in this specialization, we can start solving real world problems. Thanks once more!

von Maxence L

15. Dez. 2016

Comme les précédents dans de cette spécialisation, ce cours est très riche et donne les clés pour utiliser des outils complexes et puissants. Toutefois, un peu plus de détails sur certains aspects, notamment théoriques, pourraient améliorer la compréhension de certains chapitres plus techniques.

von Alexandru I

25. Sep. 2020

I think all the advanced concepts presented in this course were a little bit rushed. Maybe I would have been better had we received more information, meaning more lecture materials. But over all, I feel really good about choosing this Specialization.

von Steve S

26. Aug. 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 Martin B

11. Apr. 2019

Greatly enjoyed it. As with the other courses in this specialization the discussion of the subjects is impeccable, especially if you've taken some preparatory mathematics courses. The reliance on Graphlab Create is a drag though.

von Raj

27. Mai 2017

Clustering & Retrieval was a lot tougher compared to courses on regression & classification because the match concepts behind this course were too complex. Nevertheless Emily tried to make this course as intuitive as possible

von Abhishek S

10. Feb. 2018

Till Expectation Maximization, the learning is tremendous. However, once past that, everything would feel incomplete since most assignments are spoon fed after that. Rating it four stars because of initial lectures.

von Siva J

26. Feb. 2017

Good and deep dive into ML!

Absolutely disappointed that the course was delayed and the promise to take it through Course 5 and Capstone Project didn't come through.

Not at all happy with that!!

von Srinivas C

7. Jan. 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 Ahmad A

31. März 2017

This course was my first encounter with Machine Learning! The course gave me a good understanding of the different ML algorithms used in clustering and retrieval of data!

von Andrey

9. Apr. 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.

von Marco A d S M

20. Okt. 2017

As explicações poderiam ser um pouco mais detalhadas neste curto. Tive certa dificuldade em alguns conceitos apresentados, mais do que nos outros cursos.

von Keith D

19. Juni 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 Manish G

15. Jan. 2020

This topic was very deep and I learnt many complex algos. Would suggest to have some more examples for the algorithms presented in this modules.

von Marcin W

9. Aug. 2016

Very good course. Too long interval between modules make hard for non-Python developers. Easy to forget some of the Python structures.

von Farrukh N A

17. März 2017

Great course on machine learning, however, left us in middle of learning, Recommender System + Deep Learning Capstone is missing

von Iurii S

26. Nov. 2017

Good course overall.

Starting to get more on the side of being mostly implemented and only needing to insert a line or two.

von Ayush K G

24. Feb. 2018

At some topics more explaination (eg. Map reduce and LDA) needed although as a whole it is good course.

von Big O

21. Dez. 2018

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

von Pan D

10. Okt. 2021

Although the concept is good, datasets and code in assignments are modified and give strange result

von Michael B

4. Sep. 2016

Good survey of the material, but assignments are superficial and don't test thorough understanding.

von Peter

26. Juli 2016

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