Machine Learning for Financial Risk Management with Python

In the age of data-driven problem-solving, applying sophisticated computational tools for explaining substantive phenomena is a valuable skill. Yet, application of methods assumes an understanding of the data, structure, and patterns that influence the broader research program. This Element offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered in this Element, in addition to R code and real data to facilitate interaction with the concepts. Upon setting the stage for clustering, the following algorithms are detailed: agglomerative hierarchical clustering, k-means clustering, Gaussian mixture models, and at a higher-level, fuzzy C-means clustering, DBSCAN, and partitioning around medoids (k-medoids) clustering.

This book introduces Raspberry Pi, using real world applications in computer vision, machine learning, and deep learning. It provides a detailed, step-by-step, approach to application development for

Start with a Solid Foundation to Secure Your CISSP!The Effective CISSP: Security and Risk Management is for CISSP aspirants and those who are interested in information security or confused by cybersec

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for grad

Python is today's fastest growing programming language. This engaging and refreshingly different guide breaks down the skills into clear step-by-step chunks and explains the theory using brief easy-to-understand language. Rather than bamboozling readers with pages of mind-numbing technical jargon, this book includes 150 practical challenges, putting the power in the reader's hands. Through creating programs to solve these challenges the reader will quickly progress from mastering the basics to confidently using subroutines, a graphical user interface, and linking to external text, csv and SQL files. This book is perfect for anyone who wants to learn how to program with Python. In particular, students starting out in computer science and teachers who want to improve their confidence in Python will find here a set of ready-made challenges for classroom use.

A groundbreaking, flexible approach to computer science and data scienceThe Deitels’ Introduction to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud o

Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you.Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems.In this book, you will:Use heuristics and design fitness functions.Build genetic algorithms.Make nature-inspired swarms with ants, bees and particles.Create Monte Carlo simulations.Investigate cellular automata.Find minima and maxima, using hill climbing and simulated annealing.Try selection methods,

Strengthen mathematical skills and gain practice using those skills in preparation for success in machine trades or manufacturing with Peterson/Smith's MATHEMATICS FOR MACHINE TECHNOLOGY, 8E. This com

Build machine learning (ML) solutions for Java development. This book shows you that when designing ML apps, data is the key driver and must be considered throughout all phases of the project life cyc

Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning

Finance and Risk Management for International Logistics and the Supply Chain presents a detailed overview of financial and risk management tools, activity-based costing, and multi-criteria decision-ma

An indispensable guide for engineers and data scientists in design, testing, operation, manufacturing, and maintenanceA road map to the current challenges and available opportunities for the research

Foreword by Oliver Schabenberger, PhDExecutive Vice President, Chief Operating Officer and Chief Technology Officer SASDive into deep learning! Machine learning and deep learning are ubiquitous in our

Enterprise risk management has never been as topical as it currently is. The 2008 financial crisis, ever-present cyber-security threats, market volatility, increasing regulation, climate change, stake

This book combines the fundamentals of finance with relevance and effectiveness. It allows for the practice of this subject and covers all the programs of business schools, universities' finance cours

This edition of Supply Chain Management (SCM) was revised to appeal to a wider readership besides students taking SCM courses. Global supply chain managers and researchers in the fields of SCM and operations strategy would find it a useful reference. Rather than discuss the technical issues of SCM, the book focuses on the strategic perspectives and approaches of SCM. Students learn to identify SCM issues from the top management's perspective. The book also presents real-world managerial problems and incorporates case studies for connecting theories with practices. By exploring the fundamental issues of SCM, managers acquire a new learning perspective that enables them to solve problems in a more sustainable and innovative manner rather than use short-term, ad hoc solutions. Finally, it distils various theoretical concepts to allow researchers to observe real SCM issues in a managerial context which allows for practical, meaningful and impactful research to be carried out.

This edition of Supply Chain Management (SCM) was revised to appeal to a wider readership besides students taking SCM courses. Global supply chain managers and researchers in the fields of SCM and operations strategy would find it a useful reference. Rather than discuss the technical issues of SCM, the book focuses on the strategic perspectives and approaches of SCM. Students learn to identify SCM issues from the top management's perspective. The book also presents real-world managerial problems and incorporates case studies for connecting theories with practices. By exploring the fundamental issues of SCM, managers acquire a new learning perspective that enables them to solve problems in a more sustainable and innovative manner rather than use short-term, ad hoc solutions. Finally, it distils various theoretical concepts to allow researchers to observe real SCM issues in a managerial context which allows for practical, meaningful and impactful research to be carried out.

Direct from Microsoft, this Exam Ref is the official study guide for the Microsoft 70-774 Perform Cloud Data Science with Azure Machine Learning certification exam, the second of two exams required fo

This book presents a broad overview of risk management in the banking industry, with a special focus on strategic thinking and decision-making. It reveals the broader context behind decision models a

This book simultaneously introduces C++ programming techniques and numerical techniques for derivative pricing and risk management. The techniques covered include Monte Carlo pricing, finite differenc

Finally! Deep Neural Networks Simplified with PythonDeep Learning Step by Step with Python takes you on a gentle, fun and unhurried journey to building your own deep neural network models in Python. U

Managing risk in and across organizations has always been of vital importance, both for individual firms and for the globalized economy more generally. With the global financial crisis, a dramatic les

Risk management as a discipline is evolving, evolving to deal with fat tails, evolving to deal with macro-economic drivers and basic forms of nonlinearity. And yet, financial risk management remains l

With the fall of trade barriers, global competition and company shareholder demands have become more intense, causing business leaders to seek further value generation within their supply chains and t

Get started with Python for data analysis and numerical computing in the Jupyter notebookAbout This BookLearn the basics of Python in the Jupyter NotebookAnalyze and visualize data with pandas, NumPy,

This second edition focuses on audio, image and video data, the three main types of input that machines deal with when interacting with the real world. A set of appendices provides the reader with sel

Even though traditional microfinance has successfully paved the way for offering financial services to low-income populations without traditional collateral, many microfinance institutions (MFIs) are

If you are a programmer who wants to get started with data mining, then this book is for you.

Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk a

XVA Desks: A New Era for Risk Management is a comprehensive guide to the fundamentals and latest developments in this rapidly expanding field. Written by a seasoned practitioner, it begins with an ove

One book gives you a solid understanding of how derivatives are used to manage the risks of financial decisions. Extremely reader friendly, market-leading INTRODUCTION TO DERIVATIVES AND RISK MANAGEME

The impact of the global financial crisis on government funds has been significant, with squeezed budgets having to satisfy ever-increasing demands for public services. Managers working in the public

"The impact of the global financial crisis on government funds has been significant, with squeezed budgets having to satisfy ever-increasing demands for public services. Managers working in the public

The modeling of dependence structures (or copulas) is undoubtedly one of the key challenges for modern financial engineering. First applied to credit-risk modeling, copulas are now widely used across

Balanced, practical risk management for post – financial crisis institutionsFundamentals of Risk Management fills a critical gap left by existing risk management texts. Instead of focusing only on qua

How do you, as a busy security executive or manager, stay current with evolving issues, familiarize yourself with the successful practices of your peers, and transfer this information to build a knowl

Managing Energy Risk is a practical guide to using modern techniques in financial mathematics for trading energy. Following the successful first edition of the book, this second edition catches up wit

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible t