Informações sobre o curso
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Learner Career Outcomes

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comecei uma nova carreira após concluir estes cursos

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Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível intermediário

Aprox. 18 horas para completar

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

Inglês

Legendas: Inglês

Habilidades que você terá

Python ProgrammingPrincipal Component Analysis (PCA)Projection MatrixMathematical Optimization

Learner Career Outcomes

50%

comecei uma nova carreira após concluir estes cursos

48%

consegui um benefício significativo de carreira com este curso

100% on-line

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível intermediário

Aprox. 18 horas para completar

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

Inglês

Legendas: Inglês

Programa - O que você aprenderá com este curso

Semana
1
5 horas para concluir

Statistics of Datasets

8 vídeos (Total 27 mín.), 6 leituras, 4 testes
8 videos
Welcome to module 141s
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!27s
6 leituras
About Imperial College & the team5min
How to be successful in this course5min
Grading policy5min
Additional readings & helpful references5min
Set up Jupyter notebook environment offline10min
Symmetric, positive definite matrices10min
3 exercícios práticos
Mean of datasets15min
Variance of 1D datasets15min
Covariance matrix of a two-dimensional dataset15min
Semana
2
4 horas para concluir

Inner Products

8 vídeos (Total 36 mín.), 1 leitura, 5 testes
8 videos
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!35s
1 leituras
Basis vectors20min
4 exercícios práticos
Dot product10min
Properties of inner products20min
General inner products: lengths and distances20min
Angles between vectors using a non-standard inner product20min
Semana
3
4 horas para concluir

Orthogonal Projections

6 vídeos (Total 25 mín.), 1 leitura, 3 testes
6 videos
Projection onto 1D subspaces7min
Example: projection onto 1D subspaces3min
Projections onto higher-dimensional subspaces8min
Example: projection onto a 2D subspace3min
This was module 3!32s
1 leituras
Full derivation of the projection20min
2 exercícios práticos
Projection onto a 1-dimensional subspace25min
Project 3D data onto a 2D subspace40min
Semana
4
5 horas para concluir

Principal Component Analysis

10 vídeos (Total 52 mín.), 5 leituras, 2 testes
10 videos
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 module42s
This was the course on PCA56s
5 leituras
Vector spaces20min
Orthogonal complements10min
Multivariate chain rule10min
Lagrange multipliers10min
Did you like the course? Let us know!10min
1 exercício prático
Chain rule practice20min
4.0
251 avaliaçõesChevron Right

Principais avaliações do Mathematics for Machine Learning: PCA

por JSJul 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 JVMay 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!

Instrutores

Avatar

Marc Peter Deisenroth

Lecturer in Statistical Machine Learning
Department of Computing

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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