This course provides a comprehensive introduction to Artificial Intelligence in Finance by integrating foundational financial concepts, mathematical modelling, and modern machine-learning techniques. This course aims to cover classical financial theory and contemporary AI techniques combined to address asset pricing, trading, portfolio management, risk assessment, and financial decision-making problems. The contents taught in the course include (not limited to) time value of money, financial instruments, derivatives, pricing, stochastic processes, and the Black–Scholes framework before progressing to Monte Carlo methods, volatility modelling, portfolio optimization (Classical Markowitz theory and modern approaches), risk measures, forecasting, backtesting and algorithmic trading.

The course also examines credit risk and systemic financial risk using methods such as XGBoost and graph neural networks. Advanced modules cover synthetic financial data generation, stress testing, and emerging agentic finance systems.

Students are expected to have completed the Foundations of Machine Learning (FoML) or Pattern Recognition and Machine Learning (PRML) course. While knowledge of Deep Learning and Reinforcement Learning is desired, it is not a mandatory prerequisite.