# 学生对 伦敦帝国学院 提供的 Mathematics for Machine Learning: PCA 的评价和反馈

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1,906 个评分
453 条评论

## 课程概述

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....

## 热门审阅

##### 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.

##### NS

Jun 19, 2020

Relatively tougher than previous two courses in the specialization. I'd suggest giving more time and being patient in pursuit of completing this course and understanding the concepts involved.

## 126 - Mathematics for Machine Learning: PCA 的 150 个评论（共 451 个）

May 24, 2020

Professor Deisenroth is amazing. Very tough course but appreciated all the derivations and explanations of concepts.

Mar 21, 2019

It was challenging but worth it to enhance the mathematic skills for machine learning. Thanks for the awesome course.

Sep 28, 2018

This was the toughest of the three modules. It gave me a strong foundation to continue pusrsuing machine learning.

May 06, 2020

This course was tough but awesome. Lots of things i learnt from this course. Great course indeed and worth doing.

Jul 30, 2020

The PCA part Was a bit tricky barely handle the concepts.

thank you imperial team for such interactive course

Aug 21, 2019

One of the most challenging course in my life - almost impossible without python and mathematics background.

Jul 29, 2020

Excellent course ... Quite challenging, a little difficult but I have learned a lot ... Thank you ...

Sep 06, 2019

Amazing course and provides basic introduction for the PCA. Need for programming help in this course.

Feb 25, 2020

Great course. I appreciate the rigor and clear mathematical explanations provided by Dr. Deisenroth.

Feb 25, 2019

exellent course! nice python wokring enviroment and very good explanation at each topic. thank you!

Jan 18, 2020

Excellent and to-the-point explanations, useful assignments to make the concepts etched in memory

Jun 20, 2020

This course helped me in getting a deeper knowledge on Principal Component Analysis. Thank You.

Oct 16, 2018

concise and to the point. Might want to introduce a bit the technique of Lagrangin multiplier

Mar 18, 2020

I learnt a lot from this course and now I think I am much more familiar with this algorithm.

Apr 23, 2020

extremely informative and really help me understand the basic math in Machine learning

Apr 17, 2020

Course was challenging, so does the math. It was a very excellent learning experience!

Nov 14, 2019

This course is also so helpful, and the lecturer is so predominant on what he taught.

Oct 20, 2019

Truly hardcore course if your are a noob in reduced order modelling. Very challenging

Nov 05, 2019

Excellent course and extremely difficult one to grasp at one go. Regards Arijit Bose

May 26, 2018

Very hard to follow, but you need to do it to understand machine learning very well.

Jul 27, 2019

I have thoroughly enjoyed every course of this specialization. Thank you very much.

Sep 22, 2019

Lectures and programming assignments were selected nicely to teach the math of PCA

May 22, 2019

This was really difficoult, but I'm so proud for the completion of the course.

Nov 23, 2019

the course was very much intuitive and helpful to grasp the knowledge of PCA

Jun 17, 2019

We need more elaborate explanation at few tricky places during the course.