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Mathematics for Machine Learning: PCA, Imperial College London

581 calificaciones
116 revisiones

Acerca de este Curso

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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114 revisiones

por Bryan Stafford

Feb 19, 2019

This course needs a lot of work to get to the level of quality of its two predecessors.

por Avirup Guha

Feb 18, 2019

Very poorly written/performed material with inadequate coding help. The engagement level is quite low. Will not recommend if you have novice programming background or new to math concepts.

por Prashant Dabholkar

Feb 17, 2019

The lecturer is good and probably has a very good understanding of the mathematics. However if you are looking for a light and easy course, then this one is not for you. The mathematics is sometimes difficult to follow and although the lecturer patiently explains the derivation of the results, I had to go back and forth a number of times to understand what was happening.

por paulo

Feb 11, 2019

great material but explanation are a little bit messy

por amit singh

Feb 08, 2019

Unlike the prior courses in the series, topics not clearly explained and brought too sudden. Also none of calculations shown completely, instructor just wrote results in the end. Due to all these reason I was not able to finish the course.

por Lafite

Feb 04, 2019


por Dora Jambor

Feb 04, 2019

Great course including many useful refreshers on foundational concepts like inner products, projections, Lagrangian etc.

por Patrick Frece

Feb 01, 2019

The programming tasks are very bad documented and have errors.

por 용석 권

Jan 30, 2019

Programming assignments' quality is too bad to follow it. Their lecture's explanation and assignments' notation are not matched. Moreover, the code is sometimes ridiculous.

por Manju Sharma

Jan 29, 2019

Good stuff:

Instructor has good knowledge of the subject. The course content structure is designed well.

Bad stuff:

Concepts could have been presented with more clarity. Programming assignments need more instructions and less assumption on what the students already know.