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 MaterialsEnseñado por: Samuel J. Cooper, Lecturer
Dyson School of Design EngineeringEnseñado 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 | Inglés (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
- Reading: About Imperial College & the team
- Reading: How to be successful in this course
- Reading: Grading policy
- Reading: Additional readings & helpful references
- Discussion Prompt: Nice to meet you!
- Plugin que não vale nota: Complete our short pre-course survey
- Vídeo: Motivations for linear algebra
- Teste para praticar: Solving some simultaneous equations
- Vídeo: Getting a handle on vectors
- Teste para praticar: Exploring parameter space
- Vídeo: Operations with vectors
- Teste para praticar: 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
- Teste para praticar: Dot product of vectors
- Vídeo: Changing basis
- Teste para praticar: Changing basis
- Vídeo: Basis, vector space, and linear independence
- Vídeo: Applications of changing basis
- Teste para praticar: 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
- Teste para praticar: Using matrices to make transformations
- Vídeo: Solving the apples and bananas problem: Gaussian elimination
- Vídeo: Going from Gaussian elimination to finding the inverse matrix
- Teste para praticar: Solving linear equations using the inverse matrix
- Vídeo: Determinants and inverses
- Notebook: 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
- Teste para praticar: Non-square matrix multiplication
- Vídeo: Matrices changing basis
- Vídeo: Doing a transformation in a changed basis
- Teste para praticar: Mappings to spaces with different numbers of dimensions
- Vídeo: Orthogonal matrices
- Vídeo: The Gram–Schmidt process
- Notebook: Gram-Schmidt process
- Vídeo: Example: Reflecting in a plane
- Notebook: 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?
- Teste para praticar: Selecting eigenvectors by inspection
- Vídeo: Special eigen-cases
- Vídeo: Calculating eigenvectors
- Teste para praticar: Characteristic polynomials, eigenvalues and eigenvectors
- Vídeo: Changing to the eigenbasis
- Vídeo: Eigenbasis example
- Teste para praticar: Diagonalisation and applications
- Vídeo: Introduction to PageRank
- Notebook: PageRank
- Vídeo: Summary
- Vídeo: Wrap up of this linear algebra course
- Plugin que não vale nota: Post-Course Survey
- Reading: Congratulations! Ready for course 2?
Calificado: Page Rank
Calificado: Eigenvalues and eigenvectors
Preguntas Frecuentes
Cómo funciona
Trabalho
Cada curso é como um livro didático interativo, com vídeos pré-gravados, testes e projetos.
Ajuda dos seus colegas
Conecte-se com milhares de outros aprendizes, debata ideias, discuta sobre os materiais do curso e obtenha ajuda para dominar conceitos.
Certificados
Obtenha reconhecimento oficial pelo seu trabalho e compartilhe seu sucesso com amigos, colegas e empregadores.
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
Very helpful!
Great Course
This course is very usefull for beginners in machine learning. I've learned too much from Linear algebra, and that's more important i understood the intuition of linear math. This course contains real usefull exercises in Python that can help me improve my skill in math.
Extra thanks for clear English, because i'm from Russia and don't have enough background for understanding speech, but your lecturers have beautiful language.
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LC
This is a great course for those people who want to get started with ML and need a refresher on linear algebra. It's focused on the important part without overwhelming the audience with unnecessary details. I'd highly recommend this course and also the entire specialization.