Textbooks

Textbooks for the course Introduction to Machine Learning

This page lists the  textbooks for the course Introduction to Machine Learning, which covers the fundamental concepts and techniques of machine learning, such as supervised and unsupervised learning, linear and nonlinear models, classification and regression, clustering and dimensionality reduction, and more.

Click on the name of the text book to access them

- An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani.

This book provides an accessible overview of the field of statistical learning, with a focus on practical applications and examples using the R programming language. It covers topics such as linear regression, logistic regression, resampling methods, model selection and regularization, tree-based methods, support vector machines, and unsupervised learning.

- Introduction to Machine Learning with Python by Andreas C. Müller and Sarah Guido

This book is a hands-on guide to implementing machine learning solutions using Python and its popular libraries, such as scikit-learn, numpy, pandas, and matplotlib. It covers topics such as data preprocessing, feature engineering, model evaluation and improvement, pipelines and grid search, ensemble methods, neural networks, and natural language processing.

- Hands-on Machine Learning with Scikit-Learn & TensorFlow by Aurélien Géron

This book is a comprehensive and practical introduction to machine learning, deep learning, and TensorFlow, the most popular open-source framework for building and deploying machine learning models. It covers topics such as linear and polynomial regression, logistic regression, support vector machines, decision trees and random forests, k-means and hierarchical clustering, principal component analysis, artificial neural networks, convolutional neural networks, recurrent neural networks, and reinforcement learning.