Machine Learning by Design

Machine Learning by Design: From Problem Framing to Reliable Systems is a concept-first textbook in machine learning for IOAI students, computer science undergraduates, instructors, and serious self-learners. Instead of presenting machine learning as only a catalog of models or a software toolkit, the book teaches it as a discipline of problem framing, evidence, evaluation, reliability, and system design.

The book covers task formulation, predictive modeling, loss functions, decision rules, evaluation, baselines, linear models, trees, neural networks, representation learning, foundation-model workflows, modality-specific modeling, experiments, uncertainty, reliability, and deployment-minded AI systems. Its central goal is to help readers move from a vague real-world problem to a defensible model claim and then to a reliable system.

Throughout the manuscript, the material is organized around a recurring set of practical questions: what decision is being supported, what learning task is the closest defensible proxy, what data stands in for the world, what representation exposes the relevant structure, what baseline is serious, what evidence justifies the claim, and how a model output becomes an action in a real system. The aim is to give readers both technical foundations and a durable way of thinking about building and evaluating machine learning systems.