Machine Learning Fundamentals: A Concise Introduction

by Hui Jiang

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.

420 pages, Hardcover

Published March 24, 2022

Reviews

[Hojjat Sayyadi · Rating 3 out of 5]

چهار یا پنج ماه طول کشید خوندنش. الگوریتمهای یادگیری ماشین رو جز به جز توضیح داده ولی زبان ریاضی و آماری سطح بالا واسه درکش نیاز داره.

[Sean Richardson · Rating 5 out of 5]

The first several chapters do an excellent job of providing some structure to the study of machine learning, covering math fundamentals (including information theory), general problems and principles (there is “no free lunch” in principle, but we fortunately live in a “smooth world,” so we need not work with all possible distributions) and outlining the general anatomy of a machine learning algorithm.