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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
1,135 個評分
235 個審閱

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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176 - Mathematics for Machine Learning: PCA 的 200 個評論(共 233 個)

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 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 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 Nigel H

Apr 18, 2018

I want to give this course a higher rating but I was disappointed; the production standards are as high as ever but the assignments are a bit heavy on the Python. If you are inexperienced in coding Python you may be in trouble. This is not the case for the first two courses of this specialisation. If it is the maths that concerns you .. you are in safe hands. very well taught. Thanks

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 Jyh1003040

Jul 09, 2018

Honestly this course is the one worthing attempting. However, last week's content is really messy and challenging.

創建者 francesc b

Jun 02, 2018

I found hard to follow the mathematical proofs, and without a clear step by step formula sheet the last assignment was very hard. All in all I found the course very useful, although I would have liked more intuitive comprehension rather than deep mathematical comprehension. The previous two courses I think matched the balance. Potentially this was not possible for PCA?

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 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 Chuwei L

Apr 05, 2019

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

創建者 gaurav k

Jul 03, 2019

More examples and visualization should be there to explain.

創建者 Mark P

Jul 30, 2019

This course had a lot of potential but there were a number of inconsistencies, cut/paste comment bugs, that make it more challenging than it needs to be. The comments in the notebook exercises should be triple-checked with the text above to ensure consistency of variables. Far too often these would be mixed up, or the input/output descriptions would be incorrect. Or the unit test would have different dimensions. Lectures often left out steps - e.g. "because of orthonormal basis, we can simplify and remove a bunch of terms" - how exactly? A extra few seconds of explanations would allow students to follow more closely. Notation in lectures is sloppy - sometimes terms would be missing and then the video would quietly cut to a correction. "j's" and "i's" indices were interchanged frequently making the derivations how to follow. Also, this isn't a course on unit testing - some more tests should be included to help students debug individual functions rather than relying on the final algorithm (e.g. PCA to work). It should be explained why the "1/N" term for XX^T is not necessary even though it's in the lectures. On the plus side, the added written notes were welcome and fairly well done.

創建者 Omoloro O

Aug 07, 2019

Compared to the first two courses in this specialisation, this course was not very engaging. Additionally it was often hard to see what the end-goal was and the instructor seemed to be going deep into details without making the practical reasoning behind it clear. Furthermore, a lot of the exercises involved repetitions of tasks that can easily be done by computers.

創建者 Ben H

Aug 20, 2019

This course had a lot of potential, but unfortunately the pacing, structure, and teaching was not up to the standard of the other two courses in the specialisation. The teacher is clearly very knowledgable about his subject, and seems like a really nice person, but delivers the material in a very direct, formal mathematical style. This makes it much more difficult to gain intuitive insight into the subject matter.

Given the level of the past two courses, this felt like way too big a leap. Don't get me wrong — this course is still worthwhile, but could use some refining.

創建者 Hsueh-han W

Sep 20, 2019

many steps are not clear enough that I have to spend a lot of additional time to figure out the details.

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 Gurudu S R

Sep 16, 2019

Tutor is not clear and concise on the concepts. Need more examples for Week 2 and Week 3.

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 Rafael C

Sep 24, 2019

The Classes didn't give the knowledge to solve the assignments.

創建者 Nont N

Sep 25, 2019

I am a bit disappointed by this course. The professor didn't do much to help learner understand what's the meaning of the math we are looking at. Much of the quiz is just math grinding. The programming assignment require a lot of my effort in programming, but not much on math.

I'm not saying that this course is very bad, but Compare to the previous 2 course in the Math for ML specialization, provided by the same university, this one is obviously inferior.

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 Shuyu Z

Oct 18, 2019

The videos and instructions for the assignment are not clear.

創建者 Alexander

Nov 06, 2019

Math for the sake of math. Too big jumps in calculations, too complex.

創建者 Matan A

Oct 20, 2019

The is a lot of gap from what the lecturer learn and what the assignments requires.