Machine Learning Fundamentals: A Concise Introduction provides an accessible introduction to the principles and techniques of modern machine learning. The book explains the mathematical foundations of supervised and unsupervised learning, probabilistic models, optimization methods, neural networks, deep learning, and statistical learning theory. Practical examples and concise explanations make it suitable for undergraduate and graduate students as well as researchers seeking an introduction to machine learning.

This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely “from scratch” based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.