# 課程信息

402,799 次近期查看

## 34%

### 您將獲得的技能

Eigenvalues And EigenvectorsBasis (Linear Algebra)Transformation MatrixLinear Algebra

1

# Introduction to Linear Algebra and to Mathematics for Machine Learning

5 個視頻 （總計 28 分鐘）, 4 個閱讀材料, 3 個測驗
5 個視頻
Motivations for linear algebra3分鐘
Getting a handle on vectors9分鐘
Operations with vectors11分鐘
Summary1分鐘
4 個閱讀材料
About Imperial College & the team5分鐘
How to be successful in this course5分鐘
Grading policy5分鐘
Additional readings & helpful references10分鐘
3 個練習
Exploring parameter space20分鐘
Solving some simultaneous equations15分鐘
Doing some vector operations14分鐘
2

# Vectors are objects that move around space

8 個視頻 （總計 44 分鐘）, 4 個測驗
8 個視頻
Modulus & inner product10分鐘
Cosine & dot product5分鐘
Projection6分鐘
Changing basis11分鐘
Basis, vector space, and linear independence4分鐘
Applications of changing basis3分鐘
Summary1分鐘
4 個練習
Dot product of vectors15分鐘
Changing basis15分鐘
Linear dependency of a set of vectors15分鐘
Vector operations assessment15分鐘
3

# Matrices in Linear Algebra: Objects that operate on Vectors

8 個視頻 （總計 57 分鐘）, 3 個測驗
8 個視頻
How matrices transform space5分鐘
Types of matrix transformation8分鐘
Composition or combination of matrix transformations8分鐘
Solving the apples and bananas problem: Gaussian elimination8分鐘
Going from Gaussian elimination to finding the inverse matrix8分鐘
Determinants and inverses10分鐘
Summary59
2 個練習
Using matrices to make transformations12分鐘
Solving linear equations using the inverse matrix16分鐘
4

# Matrices make linear mappings

6 個視頻 （總計 53 分鐘）, 4 個測驗
6 個視頻
Matrices changing basis11分鐘
Doing a transformation in a changed basis4分鐘
Orthogonal matrices6分鐘
The Gram–Schmidt process6分鐘
Example: Reflecting in a plane14分鐘
2 個練習
Non-square matrix multiplication20分鐘
Example: Using non-square matrices to do a projection12分鐘
4.7

# 842 條評論

### 來自Mathematics for Machine Learning: Linear Algebra的熱門評論

Excellent review of Linear Algebra even for those who have taken it at school. Handwriting of the first instructor wasn't always legible, but wasn't too bad. Second instructor's handwriting is better.

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.

### David Dye

Professor of Metallurgy
Department of Materials

### Samuel J. Cooper

Lecturer
Dyson School of Design Engineering

### A. Freddie Page

Strategic Teaching Fellow
Dyson School of Design Engineering

### 關於 伦敦帝国学院

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....

## 關於 数学在机器学习领域的应用 專項課程

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....

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