Modern Portfolio Management

Modern Portfolio Management

Machine Learning: Origins, Developments and Implications

Machine Learning: Origins, Developments and Implications is an indispensable resource for anyone seeking a comprehensive understanding of the AI revolution. Terry Benzschawel's insightful exploration of machine learning's history, mechanisms, and societal implications equips readers with the knowledge needed to navigate this transformative era. Whether you're a student, professional, or enthusiast, this book offers a compelling journey through the intricacies of AI that will leave you enlightened and inspired.

This is a pre-order. The book isn’t being published until November 1st

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Celebrated Risk Books author, Terry Benzschawel, returns with his magnum opus, Machine Learning: Origins, Developments and Implications; a comprehensive exploration of the world of artificial intelligence and machine learning. This book investigates the historical roots, intricate mechanisms, and diverse applications of machine learning, offering readers a thorough understanding of its transformative impact not only on the world of finance but on the whole of society.


Key Areas Explored:

Fundamentals of Machine Learning:

Delve into the core concepts of machine learning, including decision trees, neural networks, and deep learning architectures. Benzschawel provides clear explanations of theoretical concepts and complex algorithms, making them accessible to both technical and non-technical readers.


Applications Across Industries:

Examine real-world applications of machine learning in various domains including healthcare, the military and marketing but with a particular focus on finance.


Ethical Implications for Financial Institutions:

Benzschawel discusses the ethical challenges arising from the integration of AI in decision-making processes and analyses the potential consequences of delegating decision authority to intelligent algorithms.


Workforce Preparedness:

Have you fostered a skilled workforce capable of harnessing AI's potential? Now is the time to begin preparing.


Policy and Governance:

This book outlines strategies to ensure the responsible and transparent use of machine learning technologies.


Interpretability and Accountability:

Transparency and interpretability in machine learning models is essential and Benzschawel discusses mechanisms to ensure accountability and mitigate biases in AI systems.

More Information
ISBN 978-1-78272-445-2
Publication date November 2023
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Terry Benzschawel

Terry is a Managing Director in Citigroup’s Institutional Clients Business. Terry heads the Portfolio Analysis and Quantitative Strategies group which develops and implements quantitative tools and strategies for credit market trading and risk management, both for Citi’s clients and for in-house applications. His financial career began in 1988 and has centered on modelling the risk and relative value of cash and synthetic debt of consumers, sovereign nations, and corporations. He began in Chase Manhattan Bank’s North American Finance Group, moved to Citibank’s Credit Card Division, and then to Salomon Brothers Fixed Income Arbitrage Group. In 1998 he moved to Citi’s Institutional Clients business, alternating among quant and strategy roles, while focusing on model-based trading, corporate debt, structured products, credit derivatives, and credit portfolio optimization. In addition to writing Credit Risk Modelling in 2012, Terry contributed to our 2003 book Credit Derivatives by Jon Gregory and is well-known in the industry.

1 Human-Machine Entanglement 
2 Machine Learning: Origins
3 Useful Tools 
4 Decision Trees
5 Introduction to Neural Networks
6 Backpropagation
7 Regularization
8 Optimization
9 Building Neural Networks
10 Early Applications of Machine Learning 
11 Interpreting Neural Network Decisions
12 Predicting Corporate Bond Returns
13 Deep Learning Networks
14 Applications of Deep Learning Networks
15 Machine Intelligence
16 Conciousness
17 The Future and its Challenges
18 The End
19 The Military
A1 ROC and CAP Curves
A2 The Chain Rule
A3 Backpropagatoin Example
A4 Convolution 
A5 Modelling the Earth
A6 The Hybrid Default Probability Model