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Mathematics for Machine Learning: PCA, Imperial College London

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681 calificaciones
137 revisiones

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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 revisiones

por 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.

por JV

May 01, 2018

This course was definitely a bit more complex, not so much in assignments but in the core concepts handled, than the others in the specialisation. Overall, it was fun to do this course!

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133 revisiones

por Yana Khalitova

Apr 18, 2019

Not really well structured. Too much in-depth details, too little intuition given. Didn't help to understand PCA. Had to constantly look for other resources online. Pity, because first 2 courses in the specialisation were really good.

por Cécile Logé

Apr 14, 2019

Amazing topic, great teachers and nice videos, but assignments can be slightly frustrating and some aspects (matrix calculus, derivatives, etc.) are really expedited... Still worth your time!!!

por Ajay Sharma

Apr 09, 2019

Great course for every one

por Chuwei Liu

Apr 05, 2019

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

por Yiqing Wang

Mar 28, 2019

The teaching is good but some programming assignment is not so good

por Ткаченко Вячеслав Евгеньевич

Mar 24, 2019

Algebra course is excellent. Calculus course is good. PCA is so bad that I am still upset that I spent my time on it.

por J A Marin

Mar 21, 2019

Solid conceptual explanations of PCA make this course stand out. The thorough review of this content is a must for any serious data researcher.

por David Hwang

Mar 21, 2019

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

por Akash Gupta

Mar 20, 2019

awesome

por Bálint - Huba Furdek

Mar 20, 2019

Great !