β Scribed by Matthew F. Dixon, Igor Halperin and Paul Bilokon
No coin nor oath required. For personal study only.
Machine Learning in Finance: From Theory to Practice Instructor's Manual
Matthew F. Dixon, Igor Halperin and Paul Bilokon
Introduction
Advantage of the Book
Recommended Course Syllabus
Overview of the Textbook
Exercises
Python Notebooks
Instructor Materials
Part I Machine Learning with Cross-Sectional Data
Introduction
Probabilistic Modeling
Bayesian Regression & Gaussian Processes
Programming Related Questions
Feedforward Neural Networks
Programming Related Questions
Interpretability
Programming Related Questions
Part II Sequential Learning
Sequence Modeling
Probabilistic Sequence Modeling
Advanced Neural Networks
Programming Related Questions
Part III Sequential Data with Decision-Making
Introduction to Reinforcement learning
Applications of Reinforcement Learning
Inverse Reinforcement Learning and Imitation Learning
π SIMILAR VOLUMES
instructor's solution manual and (mostly) python sources, officially obtained through Springer.com
This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothe
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official instructor's manual for "Machine Learning for Text" (2017), directly obtained through www.springer.com website