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学生对 Imperial College London 提供的 Mathematics for Machine Learning: PCA 的评价和反馈

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3,155 个评分

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 16, 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.

WS

Jul 6, 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.

筛选依据：

526 - Mathematics for Machine Learning: PCA 的 550 个评论（共 791 个）

创建者 Mohamed F

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Sep 30, 2022

Excellent course, but the last assignment wasn't obvious

创建者 Dave D

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May 30, 2020

This course was a fair overview of a very complex topic.

创建者 ADITYA K

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May 13, 2020

It is very informative and hands-on based Course for PCA

创建者 Saiful B I

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May 4, 2020

Not as good as the other two courses..but interesting!

创建者 Sharon P

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Sep 24, 2018

Mathematically challenging, but satisfying in the end.

创建者 Paulo Y C

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Feb 11, 2019

great material but explanation are a little bit messy

创建者 Anas E j

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Jun 19, 2022

Thank you for this course , hope to learn more !

创建者 taeha k

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Jul 27, 2019

Good but slightly less deeper than the other two

创建者 Eddery L

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May 23, 2019

The instructor is great. HW setup sucks though.

创建者 Muhammad B A

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Mar 26, 2023

its was soo hard your background not from math

创建者 manish c

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May 6, 2020

Best course for machine learning enthusiast

创建者 Thijs S

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Sep 28, 2020

The last assignment could use improvement.

创建者 andre w

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Mar 27, 2022

a really good course but also really hard

创建者 J N B P

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Sep 9, 2020

Good for intermediates in linear algebra.

创建者 Romesh M P

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Jan 16, 2020

Too much non-video lectures (lot to read)

创建者 Apriandi R A

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Mar 26, 2023

Overall very fun and make a little dizzy

创建者 3047 T

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Jul 13, 2020

The last course could have been better.

创建者 no O

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Jul 9, 2020

Challenging but in a good way.

创建者 Muhammad F T S

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Mar 28, 2021

this was hard but insightful

创建者 Deleted A

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Jan 22, 2019

Good, short, overview of PCA

创建者 Changson O

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Jan 27, 2019

Many errors of homework

创建者 Poomphob S

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Jun 18, 2020

so challenging for me

创建者 Sammy R

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Dec 25, 2019

Needs more details

创建者 Diablo

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Jun 10, 2025

good enough