- Ashwin Rao
- Adjunct Professor, ICME
- Stanford University
- Office: Huang Engineering Center, ICME Mezannine Level, Room M05
- Email: ashwin.rao@stanford.edu

My academic background is in Algorithms Theory and Abstract Algebra.

My current academic interests lie in the broad space of A.I. for Sequential Decisioning under Uncertainty.

I am particularly interested in Deep Reinforcement Learning applied to Financial Markets and to Retail Businesses.

More details on my background and work are described on my LinkedIn page.

I teach CME 241 (Foundations of Reinforcement Learning with Applications in Finance) every Winter Quarter.

- Overview (CME 241 Lecture Slides)
- Design Paradigms for Applied Mathematics implementations in Python

- Chapter 1: Markov Processes and Markov Reward Processes (CME 241 Lecture Slides)
- Chapter 2: Markov Decision Processes (CME 241 Lecture Slides)
- Chapter 3: Dynamic Programming Algorithms (CME 241 Lecture Slides)
- Chapter 4: Function Approximation and Approximate Dynamic Programming (CME 241 Lecture Slides)

- Chapter 5: Utility Theory (CME 241 Lecture Slides)
- Chapter 6: Dynamic Asset Allocation and Consumption (CME 241 Lecture Slides)
- Chapter 7: Derivatives Pricing and Hedging (No-Arbitrage/Completeness Slides, CME 241 Lecture Slides)
- Chapter 8: Order-Book Trading Algorithms (CME 241 Lecture Slides)

- Chapter 9: Monte-Carlo (MC) and Temporal-Difference (TD) for Prediction (CME 241 Lecture Slides)
- Chapter 10: Monte-Carlo and Temporal-Difference for Control (CME 241 Lecture Slides)
- Chapter 11: Batch RL, Experience-Replay, DQN, LSPI, Gradient TD (CME 241 Lecture Slides)
- Chapter 12: Policy Gradient Algorithms (CME 241 Lecture Slides)

- Chapter 13: Multi-Armed Bandits: Exploration versus Exploitation (CME 241 Lecture Slides)
- Chapter 14: Blending Learning and Planning (CME 241 Lectue Slides)
- Chapter 15: Summary and Real-World Considerations

- Moment-Generating Function and it's Applications
- Function Approximations as Affine Spaces
- Portfolio Theory (Lecture Slides)
- Introduction to and Overview of Stochastic Calculus Basics (Lecture Slides)
- The Hamilton-Jacobi-Bellman (HJB) Equation
- Black-Scholes Equation and it's Solution for Call/Put Options
- Conjugate Priors for Gaussian and Bernoulli Distributions