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Volver a Mathematics for Machine Learning: PCA

Opiniones y comentarios de aprendices correspondientes a Mathematics for Machine Learning: PCA por parte de Imperial College London

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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....

Principales reseñas


6 de jul. de 2021

Now i feel confident about pursuing machine learning courses in the future as I have learned most of the mathematics which will be helpful in building the base for machine learning, data science.


16 de jul. de 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.

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551 - 575 de 706 revisiones para Mathematics for Machine Learning: PCA

por Nicholas K

27 de abr. de 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.

por Paryant

2 de dic. de 2020

Instructor made a good attempt to cover these complex topic. However, these topics should be supported with more examples and also provide more intuitive examples as in previous 2 courses.

por Adrian C

22 de sep. de 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.

por Jean D D S

31 de ago. de 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.

por David N

5 de may. de 2021

Difficult course even having completed the two courses that precede it. Some concepts introduced here as assumed knowledge that were not covered in the prior courses.

por Dom D

21 de jul. de 2020

The week 2 code was more difficult than the other weeks. The forums are no longer attended by the professors. The access to materials from IC is great.

por Wang Z

8 de jul. de 2018

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

por 詹閔翔

17 de ene. de 2021

Thank for the excellent course content but i think it would be nice if teacher could do more example or apply than just math formula introduction

por Iurii S

26 de mar. de 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.

por bowman

27 de ago. de 2020

this is a great course except the assignment has quite a few bugs and the videos are too short and lack many topics, and the quiz are too short

por Zohair A

15 de jun. de 2020

The First 2 courses of this specialization were very good. I really wish the instructor for this course went into a little more depth.

por Francisco F

26 de abr. de 2020

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


21 de abr. de 2019

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


3 de ene. de 2021

Course is very high level. many concepts were not understood especially in the last course. Assignments were many confusing.

por Ana P A

22 de abr. de 2019

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

por Chuwei L

5 de abr. de 2019

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

por SYED H

17 de sep. de 2020

The course needs to introduce more advanced technique and practical examples or create a new Advanced course on this


9 de jul. de 2018

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

por Panagiotis T

26 de abr. de 2022

T​he difficult parts of the course where very superficially discussed in the videos and where lucking examples.

por Hsueh-han W

20 de sep. de 2019

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

por Thiha N

18 de ene. de 2022

I rarely use Discussion Forums. But for this course, I learnt all from the Discussion Forums. :(

por Alfian A H

25 de mar. de 2021

I think this course has many flaws. Some of the explanation from instructor isn't very clear.

por Saurabh M

11 de oct. de 2020

This course is pretty hard. The most important pre-requisite for this course is persistence.

por Gurudu S R

16 de sep. de 2019

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

por Vishesh K

13 de mar. de 2020

Good Content but isnt't explained well. if you are motivated by yourself then go for it.