Acerca de este Curso
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Comienza de inmediato y aprende a tu propio ritmo.

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Nivel principiante

Aprox. 22 horas para completar

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

Inglés (English)

Subtítulos: Inglés (English)

Habilidades que obtendrás

Eigenvalues And EigenvectorsBasis (Linear Algebra)Transformation MatrixLinear Algebra

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel principiante

Aprox. 22 horas para completar

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

Inglés (English)

Subtítulos: Inglés (English)

Programa - Qué aprenderás en este curso

Semana
1
2 horas para completar

Introduction to Linear Algebra and to Mathematics for Machine Learning

5 videos (Total 28 minutos), 4 lecturas, 3 cuestionarios
5 videos
Motivations for linear algebra3m
Getting a handle on vectors9m
Operations with vectors11m
Summary1m
4 lecturas
About Imperial College & the team5m
How to be successful in this course5m
Grading policy5m
Additional readings & helpful references10m
3 ejercicios de práctica
Exploring parameter space20m
Solving some simultaneous equations15m
Doing some vector operations14m
Semana
2
2 horas para completar

Vectors are objects that move around space

8 videos (Total 44 minutos), 4 cuestionarios
8 videos
Modulus & inner product10m
Cosine & dot product5m
Projection6m
Changing basis11m
Basis, vector space, and linear independence4m
Applications of changing basis3m
Summary1m
4 ejercicios de práctica
Dot product of vectors15m
Changing basis15m
Linear dependency of a set of vectors15m
Vector operations assessment15m
Semana
3
3 horas para completar

Matrices in Linear Algebra: Objects that operate on Vectors

8 videos (Total 57 minutos), 3 cuestionarios
8 videos
How matrices transform space5m
Types of matrix transformation8m
Composition or combination of matrix transformations8m
Solving the apples and bananas problem: Gaussian elimination8m
Going from Gaussian elimination to finding the inverse matrix8m
Determinants and inverses10m
Summary59s
2 ejercicios de práctica
Using matrices to make transformations12m
Solving linear equations using the inverse matrix16m
Semana
4
6 horas para completar

Matrices make linear mappings

6 videos (Total 53 minutos), 4 cuestionarios
6 videos
Matrices changing basis11m
Doing a transformation in a changed basis4m
Orthogonal matrices6m
The Gram–Schmidt process6m
Example: Reflecting in a plane14m
2 ejercicios de práctica
Non-square matrix multiplication20m
Example: Using non-square matrices to do a projection12m
4.7
670 revisionesChevron Right

33%

comenzó una nueva carrera después de completar estos cursos

33%

consiguió un beneficio tangible en su carrera profesional gracias a este curso

Principales revisiones sobre Mathematics for Machine Learning: Linear Algebra

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.

por CSApr 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.

Instructores

Avatar

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

Acerca de 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....

Acerca de Programa especializado Matemática aplicada al aprendizaje automático

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....
Matemática aplicada al aprendizaje automático

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.

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