Practice Labs: Math for ML (MML Book)

Source PDF: Mathematics for Machine Learning Book: Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong (Cambridge University Press, 2020)

This book covers the mathematical foundations that underpin machine learning: linear algebra, calculus, probability, optimization, and four central ML algorithms (regression, PCA, GMM, SVM).

Labs

Lab	Topic	Book Chapter	Key Concepts
Lab 01	Linear Algebra	Ch 2	Systems of equations, Gaussian elimination, vector spaces, basis, rank, linear mappings
Lab 02	Analytic Geometry	Ch 3	Norms, inner products, Gram-Schmidt, orthogonal projections, rotations
Lab 03	Matrix Decompositions	Ch 4	Eigenvalues, Cholesky, SVD, low-rank approximation
Lab 04	Vector Calculus	Ch 5	Gradients, Jacobians, backpropagation, Hessians, Taylor series
Lab 05	Probability & Distributions	Ch 6	Bayes’ theorem, Gaussian, exponential family, conjugacy
Lab 06	Continuous Optimization	Ch 7	Gradient descent, momentum, Lagrange multipliers, Newton’s method, convexity
Lab 07	Linear Regression	Ch 8-9	MLE, Bayesian regression, overfitting, cross-validation
Lab 08	PCA	Ch 10	Maximum variance, projection, scree plot, high-dimensional PCA
Lab 09	Gaussian Mixture Models	Ch 11	GMM, EM algorithm, K-Means, model selection
Lab 10	Support Vector Machines	Ch 12	Margin maximization, hinge loss, kernels, soft margin

How to Use

1. Each lab is a Jupyter notebook with theory (markdown) and fully implemented code cells

2. Read the theory cells, study the implementations, and run each cell

3. Open in Jupyter: jupyter notebook lab_01_linear_algebra.ipynb

Prerequisites

• Python 3.8+

• NumPy

• Matplotlib

Suggested Order

The book has two parts. Follow Part I (math foundations) first, then Part II (ML applications):

Part I: Mathematical Foundations

1. Lab 01 - Linear Algebra (the language of ML)

2. Lab 02 - Analytic Geometry (geometry of data)

3. Lab 03 - Matrix Decompositions (factoring matrices)

4. Lab 04 - Vector Calculus (training and optimization)

5. Lab 05 - Probability & Distributions (uncertainty)

6. Lab 06 - Optimization (finding best parameters)

Part II: Central ML Problems 7. Lab 07 - Linear Regression (prediction) 8. Lab 08 - PCA (dimensionality reduction) 9. Lab 09 - Gaussian Mixtures (density estimation) 10. Lab 10 - SVM (classification)