Machine Learning: Origins, Developments and Implications
First published:
ISBN: 978-1-78272-445-2
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Epilogue
Epilogue
Preface
Introduction: human-machine entanglement
Machine learning: origins
Useful tools
Decision trees
Introduction to neural networks
Back-propagation
Regularisation
Optimisation
Building neural networks
Early applications of machine learning
Interpreting neural network decisions
Predicting corporate bond returns
Deep learning networks
Applications of deep learning networks
Machine intelligence
Consciousness
The future and its challenges
Artificial intelligence and the military
Final thoughts
Appendix
Epilogue
Acknowledgements
The book began by considering the societal implications of artificial intelligence (AI) and the need for machine learning education. These included the possibility of machines turning against their creators or other maladaptive behaviour. We turned to history for an explanation of the exceptionally rapid changes we have been experiencing (at least relative to previous humans). It was argued that Harari’s timeline, transformed in logarithmic units, implies that we shall be going through several periods of volatility for the next 5–10 years, after which the episodes will decrease in frequency. These issues were raised again, beginning with machine intelligence in Chapter 15, followed by consciousness in Chapter 16 and the implications for the future in Chapter 17.
The technical portion of the book in Chapters 2–14 is a comprehensive treatment of topics, beginning with decision trees and ending with deep learning networks. The discussion begins with a description of the emergence of machine learning as a discipline, the artificial neuron, activation functions and error back-propagation. Decision-tree models were presented, followed by a discussion of early financial applications of
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