Understanding Machine Learning from theory to algorithms – Shai Shalev-Shwartz & Shai Ben-David

The subject of this book is automated learning, or, as we will more often call it, Machine Learning (ML). That is, we wish to program computers so that they can “learn” from input available to them. Roughly speaking, learning is the process of converting experience into expertise or knowledge. The input to a learning algorithm is training data, representing experience, and the output is some expertise, which usually takes the form of another computer program that can perform some task. Seeking a formal-mathematical understanding of this concept, we’ll have to be more explicit about what we mean by each of the involved terms: What is the training data our programs will access? How can the process of learning be automated? How can we evaluate the success of such a process (namely, the quality of the output of a learning program)?

Related posts:

Deep Learning in Python - LazyProgrammer
Generative Deep Learning - Teaching Machines to Paint, Write, Compose and Play - David Foster
Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
Intelligent Projects Using Python - Santanu Pattanayak
Data Science and Big Data Analytics - EMC Education Services
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Artificial Intelligence - A Very Short Introduction - Margaret A.Boden
An introduction to neural networks - Kevin Gurney & University of Sheffield
Pattern recognition and machine learning - Christopher M.Bishop
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Foundations of Machine Learning second edition - Mehryar Mohri & Afshin Rostamizadeh & Ameet Talwalk...
Fundamentals of Deep Learning - Nikhil Bubuma
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Medical Image Segmentation Using Artificial Neural Networks
Python Machine Learning Second Edition - Sebastian Raschka & Vahid Mirjalili
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda
Deep Learning with Python - Francois Cholletf
Deep Learning with Theano - Christopher Bourez
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Introduction to Scientific Programming with Python - Joakim Sundnes
Deep Learning with Applications Using Python - Navin Kumar Manaswi
Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj
Neural Networks - A visual introduction for beginners - Michael Taylor
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Python Machine Learning Eqution Reference - Sebastian Raschka
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...