For enhanced accessibility, the book is organized into numerous bite-sized chapters, each exploring a distinct topic. The narrative follows a linear progression, with each chapter building upon content from its predecessors. This structure lends itself effectively to teaching a two-semester undergraduate or postgraduate machine learning course, while remaining equally relevant to those engaged in active research or in self-study. To fully grasp machine learning, a certain level of mathematical understanding is required. The book provides a self-contained introduction to probability theory, and includes appendices summarizing useful results in linear algebra, calculus of variations, and Lagrange multipliers. However, the focus of the book is on conveying a clear understanding of ideas rather than mathematical rigor, with emphasis on real-world practical value of techniques rather than abstract theory. Complex concepts are presented from multiple perspectives including textual descriptions, diagrams, mathematical formulae, and pseudo-code to cater to readers from diverse backgrounds. weiterlesen