Mathematics for Machine Learning (MML)
Notebook series following the Mathematics for Machine Learning textbook by Deisenroth, Faisal, Ong.
Source PDF: mml-book.pdf
This folder is the best next step after the foundational notebooks if you want structured, rigorous math depth without immediately jumping into research-level theory.
Course Notebooks
| # | Notebook | Book Chapter | Topics |
|---|---|---|---|
| 01 | Linear Algebra | Ch 2 | Systems of equations, vector spaces, basis, rank, linear mappings |
| 02 | Analytic Geometry | Ch 3 | Norms, inner products, projections, rotations |
| 03 | Matrix Decompositions | Ch 4 | Eigenvalues, Cholesky, SVD, low-rank approximation |
| 04 | Vector Calculus | Ch 5 | Gradients, Jacobians, backpropagation, Taylor series |
| 05 | Probability | Ch 6 | Distributions, Bayes’ theorem, Gaussian, exponential family |
| 06 | Optimization | Ch 7 | Gradient descent, Lagrange multipliers, convexity |
| 07 | Linear Regression | Ch 8-9 | MLE, MAP, Bayesian linear regression |
| 08 | PCA | Ch 10 | Maximum variance, projection, dimensionality reduction |
| 09 | Gaussian Mixture Models | Ch 11 | GMM, EM algorithm, latent variables |
| 10 | Support Vector Machines | Ch 12 | Separating hyperplanes, kernels, dual formulation |
Exercises
| Notebook | Content |
|---|---|
| Exercises Part 1 | Practice problems for Ch 2-7 |
| Exercises Part 2 | Practice problems for Ch 8-12 |
| Solutions Part 1 | Solutions for Part 1 |
| Solutions Part 2 | Solutions for Part 2 |
Prerequisites
- foundational/ notebooks 01-04
- Python 3.8+, NumPy, Matplotlib
Suggested Order
Follow the course notebooks 01-10 in order. The book has two parts:
- Part I (01-06): Mathematical foundations
- Part II (07-10): Central ML problems that apply those foundations
How To Use This Folder Well
- Work in order unless you already know exactly which chapter you need.
- Treat Part I as the core and Part II as the application payoff for that theory.
- Use the practice labs when you want the concepts to become more operational.
Practice Labs
For hands-on implementations of each chapter, see practice-labs/.
Related
- cs229-course/ - ML algorithms (less math, more applied)
- advanced/ - research-level extensions of these topics
- mlpp-book/ - probabilistic perspective on ML
What Comes Next
- Continue to ../cs229-course/README.md for the algorithm-focused layer.
- Continue to ../advanced/README.md if you want research-oriented theory after this.
- Continue to ../mlpp-book/README.md if you want more Bayesian and probabilistic depth.
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