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

oferecido por

Programa de cursos integrados Mathematics for Machine Learning

Imperial College London

Informações sobre o curso

4.6

1,981 classificações

•

345 avaliações

In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works.
Since we're aiming at data-driven applications, we'll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you'll write code blocks and encounter Jupyter notebooks in Python, but don't worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before.
At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning.

Comece imediatamente e aprenda em seu próprio cronograma.

Redefinir os prazos de acordo com sua programação.

Sugerido: 5 weeks of study, 2-5 hours/week...

Legendas: Inglês

Eigenvalues And EigenvectorsBasis (Linear Algebra)Transformation MatrixLinear Algebra

Comece imediatamente e aprenda em seu próprio cronograma.

Redefinir os prazos de acordo com sua programação.

Sugerido: 5 weeks of study, 2-5 hours/week...

Legendas: Inglês

Semana

1In this first module we look at how linear algebra is relevant to machine learning and data science. Then we'll wind up the module with an initial introduction to vectors. Throughout, we're focussing on developing your mathematical intuition, not of crunching through algebra or doing long pen-and-paper examples. For many of these operations, there are callable functions in Python that can do the adding up - the point is to appreciate what they do and how they work so that, when things go wrong or there are special cases, you can understand why and what to do....

5 vídeos (total de (Total 31 mín.) min), 4 leituras, 3 testes

Motivations for linear algebra3min

Getting a handle on vectors9min

Operations with vectors11min

Summary1min

About Imperial College & the team5min

How to be successful in this course5min

Grading policy5min

Additional readings & helpful references10min

Solving some simultaneous equations15min

Exploring parameter space20min

Doing some vector operations12min

Semana

2In this module, we look at operations we can do with vectors - finding the modulus (size), angle between vectors (dot or inner product) and projections of one vector onto another. We can then examine how the entries describing a vector will depend on what vectors we use to define the axes - the basis. That will then let us determine whether a proposed set of basis vectors are what's called 'linearly independent.' This will complete our examination of vectors, allowing us to move on to matrices in module 3 and then start to solve linear algebra problems....

8 vídeos (total de (Total 44 mín.) min), 4 testes

Modulus & inner product9min

Cosine & dot product5min

Projection6min

Changing basis11min

Basis, vector space, and linear independence4min

Applications of changing basis3min

Summary1min

Dot product of vectors15min

Changing basis15min

Linear dependency of a set of vectors15min

Vector operations assessment15min

Semana

3Now that we've looked at vectors, we can turn to matrices. First we look at how to use matrices as tools to solve linear algebra problems, and as objects that transform vectors. Then we look at how to solve systems of linear equations using matrices, which will then take us on to look at inverse matrices and determinants, and to think about what the determinant really is, intuitively speaking. Finally, we'll look at cases of special matrices that mean that the determinant is zero or where the matrix isn't invertible - cases where algorithms that need to invert a matrix will fail....

8 vídeos (total de (Total 58 mín.) min), 3 testes

How matrices transform space5min

Types of matrix transformation8min

Composition or combination of matrix transformations7min

Solving the apples and bananas problem: Gaussian elimination8min

Going from Gaussian elimination to finding the inverse matrix8min

Determinants and inverses12min

Summary59s

Using matrices to make transformations12min

Solving linear equations using the inverse matrix16min

Semana

4In Module 4, we continue our discussion of matrices; first we think about how to code up matrix multiplication and matrix operations using the Einstein Summation Convention, which is a widely used notation in more advanced linear algebra courses. Then, we look at how matrices can transform a description of a vector from one basis (set of axes) to another. This will allow us to, for example, figure out how to apply a reflection to an image and manipulate images. We'll also look at how to construct a convenient basis vector set in order to do such transformations. Then, we'll write some code to do these transformations and apply this work computationally....

6 vídeos (total de (Total 56 mín.) min), 4 testes

Matrices changing basis11min

Doing a transformation in a changed basis6min

Orthogonal matrices8min

The Gram–Schmidt process6min

Example: Reflecting in a plane14min

Non-square matrix multiplication10min

Mappings to spaces with different numbers of dimensions12min

comecei uma nova carreira após concluir estes cursos

consegui um benefício significativo de carreira com este curso

por NS•Dec 23rd 2018

Professors teaches in so much friendly manner. This is beginner level course. Don't expect you will dive deep inside the Linear Algebra. But the foundation will become solid if you attend this course.

por CS•Apr 1st 2018

Amazing course, great instructors. The amount of working linear algebra knowledge you get from this single course is substantial. It has already helped solidify my learning in other ML and AI courses.

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....

Quando terei acesso às palestras e às tarefas?

Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.

O que recebo ao me inscrever nesta Especialização?

Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.

Qual é a política de reembolso?

Existe algum auxílio financeiro disponível?

Mais dúvidas? Visite o Central de Ajuda ao Aprendiz.

O Coursera proporciona acesso universal à melhor educação do mundo fazendo parcerias com as melhores universidades e organizações para oferecer cursos on-line.