Book Image

Statistics for Machine Learning

By : Pratap Dangeti
Book Image

Statistics for Machine Learning

By: Pratap Dangeti

Overview of this book

Complex statistics in machine learning worry a lot of developers. Knowing statistics helps you build strong machine learning models that are optimized for a given problem statement. This book will teach you all it takes to perform the complex statistical computations that are required for machine learning. You will gain information on the statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. You will see real-world examples that discuss the statistical side of machine learning and familiarize yourself with it. You will come across programs for performing tasks such as modeling, parameter fitting, regression, classification, density collection, working with vectors, matrices, and more. By the end of the book, you will have mastered the statistics required for machine learning and will be able to apply your new skills to any sort of industry problem.
Table of Contents (16 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Monte Carlo methods


Using Monte Carlo (MC) methods, we will compute the value functions first and determine the optimal policies. In this method, we do not assume complete knowledge of the environment. MC require only experience, which consists of sample sequences of states, actions, and rewards from actual or simulated interactions with the environment. Learning from actual experiences is striking because it requires no prior knowledge of the environment's dynamics, but still attains optimal behavior. This is very similar to how humans or animals learn from actual experience rather than any mathematical model. Surprisingly, in many cases it is easy to generate experience sampled according to the desired probability distributions, but infeasible to obtain the distributions in explicit form.

Monte Carlo methods solve the reinforcement learning problem based on averaging the sample returns over each episode. This means that we assume experience is divided into episodes, and that all episodes...