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學生對 伦敦帝国学院 提供的 Mathematics for Machine Learning: PCA 的評價和反饋

4.0
1,604 個評分
358 條評論

課程概述

This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction. At the end of this course, you'll be familiar with important mathematical concepts and you can implement PCA all by yourself. If you’re struggling, you'll find a set of jupyter notebooks that will allow you to explore properties of the techniques and walk you through what you need to do to get on track. If you are already an expert, this course may refresh some of your knowledge. The lectures, examples and exercises require: 1. Some ability of abstract thinking 2. Good background in linear algebra (e.g., matrix and vector algebra, linear independence, basis) 3. Basic background in multivariate calculus (e.g., partial derivatives, basic optimization) 4. Basic knowledge in python programming and numpy Disclaimer: This course is substantially more abstract and requires more programming than the other two courses of the specialization. However, this type of abstract thinking, algebraic manipulation and programming is necessary if you want to understand and develop machine learning algorithms....

熱門審閱

JS

Jul 17, 2018

This is one hell of an inspiring course that demystified the difficult concepts and math behind PCA. Excellent instructors in imparting the these knowledge with easy-to-understand illustrations.

JV

May 01, 2018

This course was definitely a bit more complex, not so much in assignments but in the core concepts handled, than the others in the specialisation. Overall, it was fun to do this course!

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251 - Mathematics for Machine Learning: PCA 的 275 個評論(共 358 個)

創建者 Dyachkov D

May 04, 2020

Very bad course. The content of any video don't correspond to tasks, assignments. Questions are formulated badly, I could not understand anything. Estimated time is wrong, it takes much longer to understand at least something. Programming assignments are crazy.Worst course in this specialization. No offence to teacher, but this tasks are

創建者 LOS

Nov 07, 2019

Classers are good. However, the exercise platform is full of bugs. Notebook keeps disconnecting, making it unable to save the latest changes. The automatic grader requires a very specific implementation in the last notebook, which is not mentioned anywhere and can you make lose hours debugging an implementation that is otherwise correct.

創建者 Jim A

Apr 15, 2020

The course should be longer and build a stronger foundation in order for the assignments to not feel disconnected from the instruction. There was a large amount of redundancy from previous courses. The PCA instruction from week 4 needs more development/insight. Great specialization overall. Part 3 needs more work though.

創建者 Toan T L

Oct 03, 2018

Thank you to all the professors and staffs for such a wonderful program. I did learn a lot.

This last course is indeed a fun and challenging one. But it fells short compared to the other two due to some aspects which can be improved in the future.

Nevertheless, I'm glad that I can learn about PCA.

創建者 Ankit C

Apr 19, 2020

The course contents were good, but I felt the explanation was not so clear. Since PCA is a very important topic in Machine Learning, after explaining some new concept, the instructor could've solved a couple of examples with it, so that the newly registered concepts would be crystal clear.

創建者 Arnaud J

Jun 12, 2018

This course is way more brutal than the two previous courses in the specializationIt is also very mathematically oriented, it lacks the graphics / animation / intuition that was given in the first two courses.However, if you make it, you indeed have a good understanding of PCA.

創建者 Philipp A R

Mar 06, 2020

A lot of input in relatively short time, main points could be pointed out better in the videos. Assignments were tough but manageable, the instructions could be clearer and more detailed. However, being pushed to figure out things by yourself is also a learning opportunity.

創建者 Xin W

Nov 12, 2019

To me, the first 3 weeks in this course is good. But the 4th week is quite confusing. And I don't understand the applicable meaning for the materials in the 4th week. I may need to review what I learned in the 4th week and then decide whether I understand it completely.

創建者 Manju S

Jan 29, 2019

Good stuff:

Instructor has good knowledge of the subject. The course content structure is designed well.

Bad stuff:

Concepts could have been presented with more clarity. Programming assignments need more instructions and less assumption on what the students already know.

創建者 Gabriel C

Apr 24, 2020

Quality of the course is great, but I would question whether it belongs in this specialization given the huge jump in expected knowledge from the first two courses to this one. Relied alot on the forums and YouTube to gain sufficient knowledge to complete this course.

創建者 Hong L

Apr 27, 2020

The content is decent but there are some bugs in the programming assignments. Particularly the last two programming assignments. The auto-grader for the second to the last assignment passes in some input that is not of the correct form.

創建者 Pierre

Apr 10, 2020

Positive points: At the end of the module, you get a good understanding on how PCA works. It fulfill its objective.

Negative points: The assignements are poorly directed, the material is not always clearly explained.

創建者 Alexander Z

Sep 14, 2018

Good Course, but

Too less examples to do the quizes on the first run.

Programming assignments are not clearly stated, so you need unnecessary much time to succeed.

I liked the Linear Algebra & Multivariate Modul more!

創建者 Devansh v

Apr 03, 2020

The course is Satisfactory.The content is Good,no doubt about it,but many topics(both mathematical and computational) were unknown and coding assignments of Jupyter notebooks of this course(PCA) are very Buggy

創建者 Marina P

Sep 06, 2019

The course is interesting, but some of the quizzes were not done very well. After the first 2 parts of this course, which were just amazing, this one seems kind of worse, although by itself its not that bad.

創建者 Cécile L

Apr 14, 2019

Amazing topic, great teachers and nice videos, but assignments can be slightly frustrating and some aspects (matrix calculus, derivatives, etc.) are really expedited... Still worth your time!!!

創建者 Nicholas K

Apr 28, 2018

It's a shame. There's lots of good material and I learned a lot. But a staggering amount of time was wasted figuring out gaps in the instructions - portions felt more like hazing than teaching.

創建者 Adrian C

Sep 22, 2019

The derivatiion of the PCA in the last week can be broken into 2 weeks with different programming assignments to get a closer and more confident understanding of the PCA method.

創建者 Jean D D S

Aug 31, 2019

I would ask the lecturer to go on more detail on the explanations and do (more) examples.

The lecturer tends to skip a few steps during calculations and demonstrations.

創建者 Wang Z

Jul 08, 2018

The knowledge introduced in this course is really helpful. However, the programming assignments are very time consuming and not necessarily relevent

創建者 Iurii S

Mar 26, 2018

Decent explanations of PCA idea, but assignments do not provide a clear feedback of what is wrong with the implementation util you get it right.

創建者 Francisco F

Apr 26, 2020

Average quality with low regard for intuition. Content is often Wikipedia pages or references to own content (chapters of own book).

創建者 NEHAL J

Apr 21, 2019

The course was highly challenging. I wish some of the explanations were detailed and the assignments had better instructions.

創建者 Ana P A

Apr 22, 2019

The professor of other two a way better. This one skips some steps in some explanation that makes the tasks hard to do

創建者 Chuwei L

Apr 05, 2019

worse than previous courses of machine learning specialization. Really confused me when introduced the inner products.