Introduction to Machine Learning: From Math to Code

Machine learning becomes easier to reason about when the equations and the implementation are considered together. In Introduction to Machine Learning: From Math to Code, Ruye Wang builds that connection step by step, moving from mathematical tools to the algorithms that use them and the code that brings them to life.

The book ranges across regression, feature extraction, classification, neural networks, and reinforcement learning. Its emphasis is not just on applying a method, but on understanding the ideas beneath it—and how those ideas translate into working algorithms.

Start with the mathematics behind the methods

The opening section develops essential tools in equation solving and unconstrained and constrained optimization. These foundations help frame many later machine learning methods as problems of estimation, search, or optimization rather than as isolated formulas.

Follow the progression from regression to learning systems

Readers move through linear and nonlinear regression, logistic and softmax regression, and Gaussian process methods before exploring feature selection and dimensionality reduction, including principal component analysis and independent component analysis. Later chapters cover classification approaches such as nearest-neighbor methods, naïve Bayes, and support vector machines, as well as clustering, neural networks, and reinforcement learning.

See how ideas become code

A defining feature of the book is its paired attention to mathematical reasoning and algorithm implementation. Worked examples in Matlab and Python illustrate how methods are applied, while the author’s approach aims to make the connection between equations and code visible. Implementation-centered chapter problems offer readers a chance to work through algorithms themselves.

Mathematical background, close at hand

Reviews of linear algebra and probability and statistics sit alongside the main material, with optimization treated in the opening part of the book. This arrangement gives readers a place to revisit relevant concepts as they encounter them in machine learning topics.

For students and technically curious practitioners

The book is aimed primarily at upper-level undergraduate and first-year graduate students in computer science, engineering, and related fields. Practicing professionals who want a mathematically grounded account of machine learning methods and their computational implementation may also appreciate its broad, connected treatment.