# 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
2,155 個評分
532 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.

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.

## 501 - Mathematics for Machine Learning: PCA 的 525 個評論（共 528 個）

Apr 09, 2020

There is hardly any co-relation between videos and assignments, while the lectures were at high school level but the assignments were at graduate level. It is high time to revise the course contents.

Aug 19, 2018

By far the worst online course that I've ever done. Assignments require a lot of experience in Python, which is not communicated upfront. At the same time, staff doesn't provide any actual support.

Sep 24, 2019

You will spend most of your time lost.

Quizes are not clear and ill-prepared.

You will need to spend a lot of time looking for material outside of the course to actually make progress.

Apr 28, 2020

1. Not intuitive like first two programs

2. The assignments sometimes jumped concepts and were not cohesive

3. The in-lecture problems seemed rushed through

Nov 16, 2019

This course is not like other two, which explain much clearly. When I do the practice quiz and coding, I resort to find other help on the Internet.

May 18, 2019

This course is really bad and extremely hard to follow. Previous two courses were executed very well, teaching quality in this is poor.

Aug 02, 2020

Worst of the three courses. I learned much more on the internet because of the lack of examples or explanation. Just not worth it.

Dec 28, 2019

I did not register in this course to have some person read out the textbooks or dictate the derivations in the lecture videos.

Oct 18, 2020

Lowest rating as the third course was absolutely poor. Low quality and in some way non-existent instruction.

Jun 05, 2018

Too conceptual and theoretical making it difficult to understand. Examples would have helped a lot.

Jan 28, 2020

Jul 19, 2020

Previous Two Courses were better in terms of both assignments and teaching.

Jun 04, 2020

Very Poor when compared to previous two courses of this specialization.

Jan 01, 2020

This course was a disaster for me. The first two were great though.

Aug 25, 2018

Impossible to do without apparently an indepth knowledge of python.

Jul 04, 2020

Very poor teaching and overall it's the worst course I've taken

Aug 27, 2020

Very bad explanation. The assignments need more instructions.

Jul 05, 2020

k-NN assignment is full of errors and no proper explanations.

Nov 24, 2019

Jumpy instruction with little illustrations

Oct 31, 2019

Worst course I've ever taken, online or IRL

Oct 20, 2019

Poorly organized and extremely confusing

Dec 11, 2019

Assignment 1 cannot be passed!

Jun 25, 2020