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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.

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

创建者 Blade

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Feb 14, 2025

Full of bugs, explanation lacking, no tips. A total waste of time.

创建者 Soumitri C

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Dec 15, 2020

okayish teaching but grading system is absolute rubbish in Week4

创建者 Aditya P

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Jul 4, 2020

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

创建者 Ahmad O

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Aug 27, 2020

Very bad explanation. The assignments need more instructions.

创建者 Aurel N

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Jul 5, 2020

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

创建者 Wensheng Z

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Nov 24, 2019

Jumpy instruction with little illustrations

创建者 Adam C

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Oct 31, 2019

Worst course I've ever taken, online or IRL

创建者 Zecheng W

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Oct 19, 2019

Poorly organized and extremely confusing

创建者 Mingzhe D

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Dec 11, 2019

Assignment 1 cannot be passed!

创建者 Cintya k

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Mar 2, 2021

confuse , difficuld and weird

创建者 朱嘉懿

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Jun 25, 2020

The assignment worked badly.

创建者 Syed s A

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Jul 23, 2020

Assignment is not proper

创建者 Анофриев А

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Oct 1, 2019

The worst course ever

创建者 Bohdan S

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Feb 17, 2020

Worst course ever

创建者 Ankit M

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Jul 11, 2020

POOR VERY POOR

创建者 Arjunsiva S

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Oct 4, 2020

meh!