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學生對 华盛顿大学 提供的 Machine Learning: Clustering & Retrieval 的評價和反饋

4.6
1,778 個評分
305 個審閱

課程概述

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....

熱門審閱

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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251 - Machine Learning: Clustering & Retrieval 的 275 個評論(共 293 個)

創建者 Liang-Yao W

Aug 24, 2017

This course is generally good, but I do feel less smoothly guided compared to the other courses in this specialization. For most modules of this course (other than the LDA part), the lecture videos are clear as before but the programming assignments are more demanding. You will probably need helps from google, at least for the usage of graphlab's functions. But as long as you are not completely new to programming and python, you should be able to work it out fine.

However, for the module introducing the LDA model and Gibbs sampling, I find it difficult to follow. The lecturer tried to convey the concepts and intuitions without presenting the step-by-step algorithm, probably because they are too involved. But personally, I would prefer still have them to think over even if I can not understand them now.

It is also a pity that the one other course and the capstone project originally planned of this specialization are not launched in the end. I do believe the lectures will provide high-quality course content and introduce them with passion.

創建者 Hristo V

Aug 31, 2016

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

創建者 Rajkumar K

May 27, 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

創建者 Gilles D

Aug 12, 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!

創建者 Usman I

Dec 29, 2016

I am taking all courses in the specialization, and this is my fourth. I have been having a great time with materials by both instructors so far, until I came to week 5 of this course.

Despite repeated viewing, my understanding of LDA is non-existent. The first section is fine, but starting from "Bayesian inference via Gibbs sampling," for me at least, the method of instruction has gone off a cliff.

I strongly suggest soliciting feedback from learners that narrowly targets the material of this week 5 to determine if it's just me or if this is a wider problem. If it is the latter, perhaps it is time to redesign the lessons of this week.

創建者 Siva J

Feb 26, 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!!

創建者 Koen O

Aug 27, 2017

I liked it a lot

創建者 Mehul P

Sep 11, 2017

Nice explanation on clustering methods.

創建者 Michele P

Sep 02, 2017

Advanced course. The material taught in this course is more advanced compared to Regression and Classification courses. You have to invest more time in respect to the previous courses. For some topics (LDA and hierarchical clustering) I had to look for other sources in order to understand the concepts properly. However, this course is a good introduction to clustering and retrieval.

創建者 Galen S

May 08, 2017

I liked the slides.

創建者 Maxence L

Dec 15, 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.

創建者 Marco A d S M

Oct 20, 2017

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

創建者 Jack B

Mar 04, 2017

Should use pandas instead of Graph Lab Create

創建者 Christopher M

Jul 01, 2019

Doesn't go quite as deep into the details as some of the other Machine Learning courses from the University of Washington do. Overall though, the course covers a LOT of ground. and provides exposure to many different topics.

I would have liked to have seen an Optional section on the derivation of some of the math that we were given functions for on the Expectation Maximization section. The models in the hierarchical clustering section take longer to fit than is necessary for a course like this (more than 40 times as long as the instructions say it should take), maybe a larger tolerance for convergence should be specified?

創建者 Jayant S

Oct 25, 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.

創建者 Baubak G

Jul 11, 2018

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

創建者 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.

創建者 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.

創建者 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.

創建者 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.

創建者 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.

創建者 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.).

創建者 Valentina S

Aug 16, 2016

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

創建者 Xiaosong L

Aug 31, 2016

the homework is getting easy

創建者 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.