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Ahmed A: AI in Financial Decision Making

AI in Financial Decision Making

Foundation Of Media Computing

This unique compendium develops a general approach to building models of economic and financial processes, with a focus on statistical learning techniques that scale to large data sets. It introduces the key elements of a parametric statistical model: likelihood, prior, and posterior, and show how to use them to make predictions.The book covers classical techniques such as multiple regression and the Kalman filter in a clear, accessible style that has been popular with students, but also includes detailed treatments of state-of-the-art models, highlighting tree-based methods, support vector machines and kernel methods, deep learning, and reinforcement learning. Theories are supplemented by real-world examples.This reference text is useful for undergraduate, graduate and even PhD students in quantitative finance, and also to practitioners who are facing the reality that data science and machine learning are disrupting the industry.

Statistical Machine Learning With Applications In Finance

Skin, Sensing, and Structures

Humanizing Artificial Touch

Val Jonas and Lauren Bone's Risk and Earned Value is the first book to pick up on the increasing trend for integrating these two disciplines. The authors provide guidance on both disciplines to enable the risk manager or the EV practitioner to understand each other's disciplines. They then explore how to optimise risk and EV during the project life cycle and show how EV and Risk together support projects in delivering business objectives.

Risk management and earned value are useful complementary tools for managing cost and schedule slippage in projects. EVM has been used to measure cost and schedule variance but it doesn't enable the user to predict likely variances. Risk management is used to identify and handle potential risk but may not help users understand the exact consequences of a risk that is realized. Val Jonas and Lauren Bone's Risk and Earned Value is the first book to pick up on the increasing trend for integrating these two disciplines. The authors provide guidance on both disciplines to enable the risk manager or the EV practitioner to understand each other's disciplines. They then explore how to optimise risk and EV during the project life cycle and show how EV and Risk together support projects in delivering business objectives. The authors include plenty of worked examples of the methods being used and provide an international perspective that looks at project management practice in Australia, the US and Europe and recognizes the increasingly global nature of project and programme management.

Risk and Earned Value

Making Better Choices

Judgement at Work

Malware detection is exceedingly challenging, requiring deeper expertise in two domains: machine learning (ML) and malware analysis.This comprehensive collection guides ML practitioners and researchers into the state-of-the-art and underlying insights needed to work in and research problems at the intersection of machine learning and cyber security, particularly around the problem of malware targeting the Windows platform.This useful volume's contributions come from a range of experts, representing large companies, major universities, and government labs, all working at the frontiers of malware detection and remediation research.

AI in Financial Decision Making

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