Acerca de este curso: 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.
Para quién es esta clase: This course is for people who want to refresh their maths skills in linear algebra, particularly for the purposes of doing data science and machine learning, or learning about data science and machine learning. We look at vectors, matrices and how to apply these to solve linear systems of equations, and how to apply these to computational problems.
Creada por: Imperial College London
Enseñado por: David Dye, Professor of Metallurgy
Department of Materials
Enseñado por: Samuel J. Cooper, Lecturer
Dyson School of Design Engineering
Enseñado por: A. Freddie Page, Strategic Teaching Fellow
Dyson School of Design Engineering
| Información básica | Curso 1 de 3 en Mathematics for Machine Learning Specialization |
| Nivel | Principiante |
| Compromiso | 5 weeks of study, 2-5 hours/week |
| Idioma | English |
| Requerimientos de hardware | No additional hardware is needed to complete this course |
| Cómo aprobar | Aprueba todas las tareas calificadas para completar el curso. |
| Calificaciones del usuario |
Programa
SEMANA 1
Introduction to Linear Algebra and to Mathematics for Machine Learning
In 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 videos, 4 lecturas, 3 cuestionarios de práctica
- Материал для самостоятельного изучения: About Imperial College & the team
- Материал для самостоятельного изучения: How to be successful in this course
- Материал для самостоятельного изучения: Grading policy
- Материал для самостоятельного изучения: Additional readings & helpful references
- Вопрос для обсуждения: Nice to meet you!
- Неоцениваемый плагин: Complete our short pre-course survey
- Vídeo: Motivations for linear algebra
- Тренировочный тест: Solving some simultaneous equations
- Vídeo: Getting a handle on vectors
- Тренировочный тест: Exploring parameter space
- Vídeo: Operations with vectors
- Тренировочный тест: Doing some vector operations
- Vídeo: Summary
SEMANA 2
Vectors are objects that move around space
In 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 videos, 3 cuestionarios de práctica
- Vídeo: Modulus & inner product
- Vídeo: Cosine & dot product
- Vídeo: Projection
- Тренировочный тест: Dot product of vectors
- Vídeo: Changing basis
- Тренировочный тест: Changing basis
- Vídeo: Basis, vector space, and linear independence
- Vídeo: Applications of changing basis
- Тренировочный тест: Linear dependency of a set of vectors
- Vídeo: Summary
Calificado: Vector operations assessment
SEMANA 3
Matrices in Linear Algebra: Objects that operate on Vectors
Now 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 videos, 2 cuestionarios de práctica
- Vídeo: How matrices transform space
- Vídeo: Types of matrix transformation
- Vídeo: Composition or combination of matrix transformations
- Тренировочный тест: Using matrices to make transformations
- Vídeo: Solving the apples and bananas problem: Gaussian elimination
- Vídeo: Going from Gaussian elimination to finding the inverse matrix
- Тренировочный тест: Solving linear equations using the inverse matrix
- Vídeo: Determinants and inverses
- Блокнот: Identifying special matrices
- Vídeo: Summary
Calificado: Identifying special matrices
SEMANA 4
Matrices make linear mappings
In 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 videos, 2 cuestionarios de práctica
- Тренировочный тест: Non-square matrix multiplication
- Vídeo: Matrices changing basis
- Vídeo: Doing a transformation in a changed basis
- Тренировочный тест: Mappings to spaces with different numbers of dimensions
- Vídeo: Orthogonal matrices
- Vídeo: The Gram–Schmidt process
- Блокнот: Gram-Schmidt process
- Vídeo: Example: Reflecting in a plane
- Блокнот: Reflecting Bear
Calificado: Gram-Schmidt Process
Calificado: Reflecting Bear
SEMANA 5
Eigenvalues and Eigenvectors: Application to Data Problems
Eigenvectors are particular vectors that are unrotated by a transformation matrix, and eigenvalues are the amount by which the eigenvectors are stretched. These special 'eigen-things' are very useful in linear algebra and will let us examine Google's famous PageRank algorithm for presenting web search results. Then we'll apply this in code, which will wrap up the course.
9 videos, 1 lectura, 3 cuestionarios de práctica
- Vídeo: What are eigenvalues and eigenvectors?
- Тренировочный тест: Selecting eigenvectors by inspection
- Vídeo: Special eigen-cases
- Vídeo: Calculating eigenvectors
- Тренировочный тест: Characteristic polynomials, eigenvalues and eigenvectors
- Vídeo: Changing to the eigenbasis
- Vídeo: Eigenbasis example
- Тренировочный тест: Diagonalisation and applications
- Vídeo: Introduction to PageRank
- Блокнот: PageRank
- Vídeo: Summary
- Vídeo: Wrap up of this linear algebra course
- Неоцениваемый плагин: Post-Course Survey
- Материал для самостоятельного изучения: Congratulations! Ready for course 2?
Calificado: Page Rank
Calificado: Eigenvalues and eigenvectors
Preguntas Frecuentes
Cómo funciona
Задания курса
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Сертификаты
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Creadores
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.
Tarifa
| Comprar curso | |
|---|---|
| Accede a los materiales del curso | Disponible |
| Accede a los materiales con calificación | Disponible |
| Recibe una calificación final | Disponible |
| Obtén un Certificado de curso para compartir | Disponible |
Calificaciones y revisiones
Great course to start ML preparation.
Thiw as much more interesting than the linear algebra class I took a long time ago.
While not particularly difficult material, course seems designed to build intuition more than just grinding tediously through the mechanics. Grounded in practical examples. And extraordinarily well presented.
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Very helpful insight into linear algebra for ML, also introduction to numpy usage