Ch 1. Introduction and Motivation
0. Intro
1. Finding Words for Intuitions
Ch 2. Linear Algebra
0. Intro
1. Systems of Linear Equations
2. Matrices
3. Solving Systems of Linear Equations
4. Vector Spaces
5. Linear Independence
6. Basis and Rank
7. Linear Mappings
8. Affine Spaces
Ch 3. Analytic Geometry
0. Intro
1. Norms
2. Inner Products
3. Lengths and Distances
4. Angles and Orthogonality
5. Orthonormal Basis
6. Orthogonal Complement
7. Inner Product of Functions
8. Orthogonal Projections
9. Rotations
Ch 4. Matrix Decompositions
0. Intro
1. Determinant and Trace
2. Eigenvalues and Eigenvectors
3. Cholesky Decomposition
4. Eigendecomposition and Diagonalization
5. Singular Value Decomposition
6. Matrix Approximation
7. Matrix Phylogeny
Ch 5. Vector Calculus
0. Intro
1. Differentiation of Univariate Functions
2. Partial Differentiation and Gradients
3. Gradients of Vector-Valued Functions
4. Gradients of Matrices
5. Useful Identities for Computing Gradients
6. Backpropagation and Automatic Differentiation
7. Higher-Order Derivatives
8. Linearization and Multivariate Taylor Series
Ch 6. Probability and Distributions
0. Intro
1. Construction of a Probability Space
2. Discrete and Continuous Probabilities
3. Sum Rule, Product Rule, and Bayes' Theorem
4. Summary Statistics and Independence
5. Gaussian Distribution
6. Conjugacy and the Exponential Family
7. Change of Variables/Inverse Transform
Ch 7. Continuous Optimization
0. Intro
1. Optimization Using Gradient Descent
2. Constrained Optimization and Lagrange Multipliers
3. Convex Optimization
Ch 8. When Models Meet Data
0. Intro
1. Data, Models, and Learning
2. Empirical Risk Minimization
3. Parameter Estimation
4. Probabilistic Modeling and Inference
5. Directed Graphical Models
6. Model Selection
Ch 9. Linear Regression
0. Intro
1. Problem Formulation
2. Parameter Estimation
3. Bayesian Linear Regression
4. Maximum Likelihood as Orthogonal Projection
Ch 10. Dimensionality Reduction with Principal Component Analysis
Ch 11. Density Estimation with Gaussian Mixture Models
Ch 12. Classification with Support Vector Machines

MML Study Note

Archive
MML
Blog
Github

Home

Book

Mathematics For Machine Learning
Download Link
Errata

References

SaVAnNA Lab (link)
공돌이의 수학정리노트 (link)
프리드버그 선형대수학 강의 (link)
Essense of linear algebra - 3Blue1Brown (link)
모두를 위한 컨벡스 최적화 (link)