Informações sobre o curso
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Nível iniciante

Aprox. 22 horas para completar

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

Inglês

Legendas: Inglês

Habilidades que você terá

Eigenvalues And EigenvectorsBasis (Linear Algebra)Transformation MatrixLinear Algebra

100% on-line

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível iniciante

Aprox. 22 horas para completar

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

Inglês

Legendas: Inglês

Programa - O que você aprenderá com este curso

Semana
1
2 horas para concluir

Introduction to Linear Algebra and to Mathematics for Machine Learning

5 vídeos (Total 28 mín.), 4 leituras, 3 testes
5 videos
Motivations for linear algebra3min
Getting a handle on vectors9min
Operations with vectors11min
Summary1min
4 leituras
About Imperial College & the team5min
How to be successful in this course5min
Grading policy5min
Additional readings & helpful references10min
3 exercícios práticos
Exploring parameter space20min
Solving some simultaneous equations15min
Doing some vector operations14min
Semana
2
2 horas para concluir

Vectors are objects that move around space

8 vídeos (Total 44 mín.), 4 testes
8 videos
Modulus & inner product10min
Cosine & dot product5min
Projection6min
Changing basis11min
Basis, vector space, and linear independence4min
Applications of changing basis3min
Summary1min
4 exercícios práticos
Dot product of vectors15min
Changing basis15min
Linear dependency of a set of vectors15min
Vector operations assessment15min
Semana
3
3 horas para concluir

Matrices in Linear Algebra: Objects that operate on Vectors

8 vídeos (Total 57 mín.), 3 testes
8 videos
How matrices transform space5min
Types of matrix transformation8min
Composition or combination of matrix transformations8min
Solving the apples and bananas problem: Gaussian elimination8min
Going from Gaussian elimination to finding the inverse matrix8min
Determinants and inverses10min
Summary59s
2 exercícios práticos
Using matrices to make transformations12min
Solving linear equations using the inverse matrix16min
Semana
4
6 horas para concluir

Matrices make linear mappings

6 vídeos (Total 53 mín.), 4 testes
6 videos
Matrices changing basis11min
Doing a transformation in a changed basis4min
Orthogonal matrices6min
The Gram–Schmidt process6min
Example: Reflecting in a plane14min
2 exercícios práticos
Non-square matrix multiplication20min
Example: Using non-square matrices to do a projection12min
4.7
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Principais avaliações do Mathematics for Machine Learning: Linear Algebra

por PLAug 26th 2018

Great way to learn about applied Linear Algebra. Should be fairly easy if you have any background with linear algebra, but looks at concepts through the scope of geometric application, which is fresh.

por NSDec 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.

Instrutores

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David Dye

Professor of Metallurgy
Department of Materials
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Samuel J. Cooper

Lecturer
Dyson School of Design Engineering
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A. Freddie Page

Strategic Teaching Fellow
Dyson School of Design Engineering

Sobre Imperial College London

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....

Sobre Programa de cursos integrados Matemática para aprendizagem automática

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 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....
Matemática para aprendizagem automática

Perguntas Frequentes – FAQ

  • 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.

  • 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.

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