• Title Introduction to Machine Learning
• Author(s) Amnon Shashua
• Publisher: Hebrew University of Jerusalem (Fall, 2008), and arXiv.org
• Hardcover/Paperback N/A
• eBook PDF
• Language: English
• ISBN-10/ASIN: N/A
• ISBN-13: N/A
• Share This:  

Book Description

In this book, it will be exploring machine learning, the concepts that run these technologies and by the time you get to the end you will have more knowledge than many and will be equipped to start building your own applications.

It covers Statistical Inference (Bayes, EM, ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering), and PAC learning (the Formal model, VC dimension, Double Sampling theorem).

• N/A

• Amazon

• Machine Learning
• Neural Networks and Deep Learning
• Artificial Intelligence
• Data Analysis and Data Mining

• Introduction to Machine Learning (Amnon Shashua)

• The Hundred-Page Machine Learning Book (Andriy Burkov)

  Everything you really need to know in Machine Learning in a hundred pages! This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori.

• Introduction to Statistical Learning: with Applications in Python

  This book covers the same materials as Introduction to Statistical Learning: with Applications in R (ISLR) but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

• The Shallow and the Deep: Old School Machine Learning

  This book is a collection of lecture notes that offers an accessible introduction to Neural Networks and machine learning in general. The focus lies on classical machine learning techniques, with a bias towards classification and regression.

• Financial Machine Learning (Bryan T. Kelly, et al.)

  This book is designed for both financial economists interested in grasping machine learning tools, as well as for statisticians and machine learners seeking interesting financial contexts where advanced methods may be deployed.

• Distributional Reinforcement Learning (Marc G. Bellemare, et al)

  Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. This first comprehensive guide provides a new mathematical formalism for thinking about decisions from a probabilistic perspective.

• Machine Learning under Resource Constraints (Katharina Morik)

  It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering.

• An Introduction to Machine Learning Interpretability

  Understanding and trusting models and their results is a hallmark of good science. Get an applied perspective on how this applies to machine learning, including fairness, accountability, transparency, and explainable AI.

• Hyperparameter Tuning for Machine Learning: A Practical Guide

  This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods.

• Approaching (Almost) Any Machine Learning Problem

  This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book is best enjoyed with a cup of coffee and a laptop/workstation where you can code along.

• An Introduction to Quantum Machine Learning for Engineers

  This book provides a self-contained introduction to Quantum Machine Learning for an audience of engineers with a background in probability and linear algebra, describes the necessary background, concepts, and tools, covers parametrized quantum circuits, etc.