## Table of contents

- Preface
- Introduction
- 1 Notations and data
- 2 Introduction
- 3 Factor investing and asset pricing anomalies
- 4 Data preprocessing
- Common supervised algorithms
- 5 Penalized regressions and sparse hedging for minimum variance portfolios
- 6 Tree-based methods
- 7 Neural networks
- 8 Support vector machines
- 9 Bayesian methods
- From predictions to portfolios
- 10 Validating and tuning
- 11 Ensemble models
- 12 Portfolio backtesting
- Further important topics
- 13 Interpretability
- 14 Two key concepts: causality and non-stationarity
- 15 Unsupervised learning
- 16 Reinforcement learning
- Appendix
- 17 Data description
- 18 Python notebooks
- 19 Solutions to exercises