Students in my Stanford courses on machine learning have already made several useful suggestions, as have my colleague, Pat Langley, and my teaching assistants, Ron Kohavi, …

The purpose of this book is to provide you the reader with the following: a framework with which to approach problems that machine learning learning might help solve.

Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to stu-dents and nonexpert …

Machine learning addresses the issue of analyzing, reproducing and predicting var-ious mechanisms and processes observable through experiments and data acquisi-tion.

Machine learning is one way of achieving artificial intelligence, while deep learning is a subset of machine learning algorithms which have shown the most promise in dealing with problems …

Here, we will present a top-down algorithm for learning decision trees, since this is one of the most versatile, most efficient, and most popular machine learning algorithms.

These are notes for a one-semester undergraduate course on machine learning given by Prof. Miguel ́A. Carreira-Perpi ̃n ́an at the University of California, Merced.

Hardcopy (Amazon). Foundations of Machine Learning Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar MIT Press, Chinese Edition, 2019. Table of contents. Sample pages …

Here we consider a set of methods that find the solution x∗ of a single-variable nonlinear equation f(x) = 0, by searching iteratively through a neighborhood of the domain, in which x∗ is known to …

Machine learning (ML) is a branch of artificial intelligence (AI) that focuses on building systems that can learn from data and improve their performance over time without being explicitly …