PROBABILISTIC MACHINE LEARNING: AN INTRODUCTION

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Probabilistic Machine Learning 系列名：Adaptive Computation and Machine Learning series ISBN13：9780262046824 出版社：Mit Pr 作者：Kevin P. Murphy 裝訂／頁數：精裝／864頁 出版日：2022/02/01

An Introduction to Optimization: With Applications to Machine Learning (5版)

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An Introduction to Optimization Accessible introductory textbook on optimization theory and methods, with an emphasis on engineering design, featuring MATLAB® exercises and worked examples Fully updated to reflect modern developments in the field, the Fifth Edition of An Introduction to Optimization fills the need for an accessible, yet rigorous, introduction to optimization theory and methods, featuring innovative coverage and a straightforward approach. The book begins with a review of basic definitions and notations while also providing the related fundamental background of linear algebra, geometry, and calculus. With this foundation, the authors explore the essential topics of unconstrained optimization problems, linear programming problems, and nonlinear constrained optimization. In addition, the book includes an introduction to artificial neural networks, convex optimization, multi-objective optimization, and applications of optimization in machine learning. Numerous diagrams and figures found throughout the book complement the written presentation of key concepts, and each chapter is followed by MATLAB® exercises and practice problems that reinforce the discussed theory and algorithms. The Fifth Edition features a new chapter on Lagrangian (nonlinear) duality, expanded coverage on matrix games, projected gradient algorithms, machine learning, and numerous new exercises at the end of each chapter. An Introduction to Optimization includes information on: The mathematical definitions, notations, and relations from linear algebra, geometry, and calculus used in optimization Optimization algorithms, covering one-dimensional search, randomized search, and gradient, Newton, conjugate direction, and quasi-Newton methods Linear programming methods, covering the simplex algorithm, interior point methods, and duality Nonlinear constrained optimization, covering theory and algorithms, convex optimization, and Lagrangian duality Applications of optimization in machine learning, including neural network training, classification, stochastic gradient descent, linear regression, logistic regression, support vector machines, and clustering. An Introduction to Optimization is an ideal textbook for a one- or two-semester senior undergraduate or beginning graduate course in optimization theory and methods. The text is also of value for researchers and professionals in mathematics, operations research, electrical engineering, economics, statistics, and business.

INTRODUCTION TO MACHINE LEARNING AND QUANTITATIVE FINANCE

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In today's world, we are increasingly exposed to the words "machine learning" (ML), a term which sounds like a panacea designed to cure all problems ranging from image recognition to machine language translation. Over the past few years, ML has gradually permeated the financial sector, reshaping the landscape of quantitative finance as we know it. An Introduction to Machine Learning in Quantitative Finance aims to demystify ML by uncovering its underlying mathematics and showing how to apply ML methods to real-world financial data. In this book the authors Provide a systematic and rigorous introduction to supervised, unsupervised and reinforcement learning by establishing essential definitions and theorems. Dive into various types of neural networks, including artificial nets, convolutional nets, recurrent nets and recurrent reinforcement learning. Summarize key contents of each section in the tables as a cheat sheet. Include ample examples of financial applications. Showcase how to tackle an exemplar ML project on financial data end-to-end. Provide a GitHub repository https://github.com/deepintomlf/mlfbook.git that contains supplementary Python codes of all methods/examples. Featured with the balance of mathematical theorems and practical code examples of ML, this book will help you acquire an in-depth understanding of ML algorithms as well as hands-on experience. After reading An Introduction to Machine Learning in Quantitative Finance, ML tools will not be a black box to you anymore, and you will feel confident in successfully applying what you have learnt to empirical financial data! Sample Chapter(s) Preface Chapter 1: Overview of Machine Learning and Financial Applications Request Inspection Copy Contents: Preface About the Authors Acknowledgments Disclaimer Listings Overview of Machine Learning and Financial Applications Supervised Learning Linear Regression and Regularization Tree-based Models Neural Networks Cluster Analysis Principal Component Analysis Reinforcement Learning Case Study in Finance: Home Credit Default Risk Bibliography Index Readership: This textbook is suitable for MSc students or final year undergraduate students in financial mathematics, machine learning or computational finance. It would serve as a graduate textbook in introducing machine learning and its applications in quantitative finance. It may also be appropriate for those interested in pursuing a career in quantitative finance or for practitioners in the financial sector who wish to develop an in-depth understanding of machine learning and its applications to finance.