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学生对 华盛顿大学 提供的 Machine Learning: Clustering & Retrieval 的评价和反馈

4.6
2,295 个评分
392 条评论

课程概述

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....

热门审阅

BK

Aug 24, 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

Jan 16, 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.

筛选依据:

351 - Machine Learning: Clustering & Retrieval 的 375 个评论(共 380 个)

创建者 Saeed S T

Sep 7, 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.

创建者 Adrien S

Oct 7, 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.

创建者 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.

创建者 Ahmed N

Jul 17, 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.

创建者 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 :)

创建者 Dimitris Z

Jun 8, 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.

创建者 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.).

创建者 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.

创建者 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.

创建者 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.

创建者 Alexander S

Aug 7, 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.

创建者 J N B P

Oct 16, 2020

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

创建者 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.

创建者 Valentina S

Aug 16, 2016

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

创建者 Michael L

Mar 18, 2017

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

创建者 Rishabh s

Aug 13, 2020

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

创建者 Volker H

Jul 18, 2016

please rework in particular week 5, part 2

创建者 Nicolas I

Aug 31, 2016

A little too superficial and hand waving.

创建者 Harsh A

Jul 18, 2018

Too little "case-study" approach

创建者 Stuart L

Aug 30, 2016

the homework is getting easy

创建者 Rohan G L

Aug 29, 2020

I leave 2 stars as I learned a lot of new information and methods, and the theory and math behind them.

You will learn about Data Science and Machine Learning, but not much about Python.

The course is pretty much abandoned and outdated. Sframes and Turicreate packages (instructor's creations) are used instead of more universal packages. Installation in the beginning took some time and research. Many of the assignments have errors and bugs in the code that have not been updated. Forum assistance is abysmal for clarification or deeper questions. Many links are dead.

There are many times in the lectures where the instructors are writing several sentences in their handwriting on their notes instead of having the text ready to appear.

I would suggest using this course and series as a supplement to other information one as learned, not as an introduction for initial understanding. I found myself frustrated too many times.

创建者 Ryan M

Sep 16, 2020

While the topics covered in this course are arguably more complex than those in other courses in the Machine Learning specialization, I felt that the instructor did not do a good job covering the complicated material. There is a lot of statistics in this course, and the instructor seemed to assume that students would know many of the statistical terms and concepts without explaining them. I had to use a ton of outside resources to augment the videos presented as part of this course.

Furthermore, many of the assignments seemed to have errors in them. For the last programming assignment, there is no correct answer for at least one of the questions. Since there is no support from instructional staff or Coursera, this is a bit frustrating. Luckily you could pass the quiz without even answering that specific question.

创建者 Pan W

Jan 3, 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.

创建者 ryan

Sep 23, 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.

创建者 SHAHAPURKAR S M

Jun 19, 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.