Linear Algebra Fundamentals for Machine Learning
Chapter 1: Why Linear Algebra Matters in Machine Learning
Representing Data with Vectors and Matrices
Linear Algebra in Machine Learning Algorithms
Core Concepts Overview
Setting Up Your Python Environment
Quiz for Chapter 1
Chapter 2: Getting Started with NumPy for Numerical Computing
Introduction to NumPy Arrays
Creating NumPy Arrays
Array Indexing and Slicing
Basic Array Operations
Array Attributes and Shape Manipulation
Hands-on: NumPy Array Creation and Manipulation
Quiz for Chapter 2
Chapter 3: Working with Vectors
What is a Vector?
Vector Notation
Vectors in Python using NumPy
Vector Addition and Subtraction
Scalar Multiplication
Vector Norms: Measuring Length
The Dot Product
Hands-on: Vector Operations with NumPy
Quiz for Chapter 3
Chapter 4: Working with Matrices
What is a Matrix?
Matrix Notation and Dimensions
Matrices in Python using NumPy
Types of Matrices: Square, Identity, Zero
Diagonal and Triangular Matrices
Hands-on: Matrix Creation with NumPy
Quiz for Chapter 4
Chapter 5: Essential Matrix Operations
Matrix Addition and Subtraction
Scalar Multiplication
Matrix Transpose
Matrix Multiplication: The Dot Product
Properties of Matrix Multiplication
Hands-on: Matrix Operations with NumPy
Quiz for Chapter 5
Chapter 6: Systems of Linear Equations and Matrix Inverses
Representing Linear Equations with Matrices
The Concept of a Solution
The Identity Matrix Revisited
The Matrix Inverse
Conditions for Invertibility
Solving Ax = b using the Inverse
Calculating Inverses with NumPy
Solving Linear Systems with NumPy
Hands-on: Solving Systems with NumPy
Quiz for Chapter 6
This course has been archived: A newer version with updated syllabus and improved content is now available.
Vector Norms: Measuring Length
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- Introduction to Linear Algebra, Gilbert Strang, 2023 (Wellesley-Cambridge Press) - A foundational textbook offering a clear and comprehensive explanation of vector norms and their mathematical principles.
- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - This essential textbook for deep learning provides detailed coverage of vector norms, explaining their use in machine learning algorithms such as regularization.
- numpy.linalg.norm, NumPy Developers, 2024 - Official documentation for the
numpy.linalg.normfunction, demonstrating its usage for calculating L1 and L2 norms in Python. - Matrix Computations, Gene H. Golub and Charles F. Van Loan, 2013 (Johns Hopkins University Press) DOI: 10.1137/1.9781421407944 - A classic advanced reference for numerical linear algebra, offering a rigorous treatment of vector and matrix norms, including their properties and role in numerical analysis.
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