Este curso faz parte do Programa de cursos integrados Mathematics for Machine Learning

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Programa de cursos integrados Mathematics for Machine Learning

Imperial College London

Informações sobre o curso

4.0

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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 machine learning algorithms.

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Sugerido: 4 weeks of study, 4-5 hours/week...

Legendas: Inglês...

Python ProgrammingPrincipal Component Analysis (PCA)Projection MatrixMathematical Optimization

Comece imediatamente e aprenda em seu próprio cronograma.

Redefinir os prazos de acordo com sua programação.

Sugerido: 4 weeks of study, 4-5 hours/week...

Legendas: Inglês...

Semana

1Principal Component Analysis (PCA) is one of the most important dimensionality reduction algorithms in machine learning. In this course, we lay the mathematical foundations to derive and understand PCA from a geometric point of view. In this module, we learn how to summarize datasets (e.g., images) using basic statistics, such as the mean and the variance. We also look at properties of the mean and the variance when we shift or scale the original data set. We will provide mathematical intuition as well as the skills to derive the results. We will also implement our results in code (jupyter notebooks), which will allow us to practice our mathematical understand to compute averages of image data sets....

8 vídeos (Total de 27 min), 5 leituras, 4 testes

Welcome to module 1min

Mean of a dataset4min

Variance of one-dimensional datasets4min

Variance of higher-dimensional datasets5min

Effect on the mean4min

Effect on the (co)variance3min

See you next module!min

About Imperial College & the team5min

How to be successful in this course5min

Grading policy5min

Additional readings & helpful references5min

Symmetric, positive definite matrices10min

Mean of datasets15min

Variance of 1D datasets15min

Covariance matrix of a two-dimensional dataset15min

Semana

2Data can be interpreted as vectors. Vectors allow us to talk about geometric concepts, such as lengths, distances and angles to characterise similarity between vectors. This will become important later in the course when we discuss PCA. In this module, we will introduce and practice the concept of an inner product. Inner products allow us to talk about geometric concepts in vector spaces. More specifically, we will start with the dot product (which we may still know from school) as a special case of an inner product, and then move toward a more general concept of an inner product, which play an integral part in some areas of machine learning, such as kernel machines (this includes support vector machines and Gaussian processes). We have a lot of exercises in this module to practice and understand the concept of inner products....

8 vídeos (Total de 36 min), 1 leitura, 5 testes

Dot product4min

Inner product: definition5min

Inner product: length of vectors7min

Inner product: distances between vectors3min

Inner product: angles and orthogonality5min

Inner products of functions and random variables (optional)7min

Heading for the next module!min

Basis vectors20min

Dot product10min

Properties of inner products20min

General inner products: lengths and distances20min

Angles between vectors using a non-standard inner product20min

Semana

3In this module, we will look at orthogonal projections of vectors, which live in a high-dimensional vector space, onto lower-dimensional subspaces. This will play an important role in the next module when we derive PCA. We will start off with a geometric motivation of what an orthogonal projection is and work our way through the corresponding derivation. We will end up with a single equation that allows us to project any vector onto a lower-dimensional subspace. However, we will also understand how this equation came about. As in the other modules, we will have both pen-and-paper practice and a small programming example with a jupyter notebook....

6 vídeos (Total de 25 min), 1 leitura, 3 testes

Projection onto 1D subspaces7min

Example: projection onto 1D subspaces3min

Projections onto higher-dimensional subspaces8min

Example: projection onto a 2D subspace3min

This was module 3!min

Full derivation of the projection20min

Projection onto a 1-dimensional subspace25min

Project 3D data onto a 2D subspace40min

Semana

4We can think of dimensionality reduction as a way of compressing data with some loss, similar to jpg or mp3. Principal Component Analysis (PCA) is one of the most fundamental dimensionality reduction techniques that are used in machine learning. In this module, we use the results from the first three modules of this course and derive PCA from a geometric point of view. Within this course, this module is the most challenging one, and we will go through an explicit derivation of PCA plus some coding exercises that will make us a proficient user of PCA. ...

10 vídeos (Total de 52 min), 5 leituras, 2 testes

Problem setting and PCA objective7min

Finding the coordinates of the projected data5min

Reformulation of the objective10min

Finding the basis vectors that span the principal subspace7min

Steps of PCA4min

PCA in high dimensions5min

Other interpretations of PCA (optional)7min

Summary of this modulemin

This was the course on PCAmin

Vector spaces20min

Orthogonal complements10min

Multivariate chain rule10min

Lagrange multipliers10min

Did you like the course? Let us know!10min

Chain rule practice20min

4.0

por JS•Jul 17th 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 1st 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!

Imperial College London is a world top ten university with an international reputation for excellence in science, engineering, medicine and business. located in the heart of London. Imperial is a multidisciplinary space for education, research, translation and commercialisation, harnessing science and innovation to tackle global challenges.
Imperial students benefit from a world-leading, inclusive educational experience, rooted in the College’s world-leading research. Our online courses are designed to promote interactivity, learning and the development of core skills, through the use of cutting-edge digital technology....

For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.
In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them.
The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting.
The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require basic Python and numpy knowledge.
At the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning....

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