Syllabus and Lecture Notes
CF963: Learning and Computational Intelligence in Economics and Finance

• Scope: This is an evolving subject. No text book covers this modules entirely. The ideas scatter around research papers. Brabazon and O'Neil's book covers many of the topics discussed here, but it demands some knowledge in computer science. (see reading list)
• How to study: The best way to study this module is to come to lectures and labs, and read research papers. The notes provided below are no substitute to lectures, labs and readings. If you have any questions, please ask the lecturer and the tutors.
• How to prepare for exam: One way to prepare for the exam is to go through the slides and see if you remember what was taught in the lectures. If anything is unclear, read the papers with the view to improve your understanding. The exam is designed with the aim to reward knowledge. It is hoped that the better you understand the material, the higher marks you will get.
• Save the planet: The lecture notes have been made editable. I can understand some people prefer to have printed material, but please help to save our planet by not printing the notes if you can.

Part I Fundamentals


1. What is Computational Finance and Economics (video / Introductive slides 1 / slides 2 / Bracil Homepage)
2. New Ways to Study Economics (slides / market science paper / Olsen's Insight)
3. Combinatorial Explosion -- Limitations of Computation (slides / computation paper / Lab1)
4. Bounded Rationality -- Economics Foundation (slides / rationality paper / web)
5. Machine Learning Basics (slides / spreadsheet / web / background: Mitchell's book)

   Part II Applications

6. Forecasting (slides / overview paper / EDDIE paper / web)
   Material by Dr Kampouridis: EDDIE demo / ppt
7. Learning Scarce Opportunities (EDDIE-ARB slides / Repository Method slides / Repository Method paper / Arbitrage paper)
8. Directional Changes (including algorithmic trading) (DC Definitions / DC Slides / Demo / Video)
9. Event Calculus (Event Calculus Slides / event calculus paper)
   Powerpoints by Ao Han: 2 / 3
10. Portfolio Optimization (slides by Tsang) (paper by Dr Alentorn / slides / pdf) (Optimization in finance and economics) (background: Maringer's book)
11. Modelling, Simulation and machine learning (slides / Web)
12. Automated Bargaining (overview slides / GP-bargain slides / paper / web)
13. Economic Wind-tunnels (slides / Cards paper / Artificial Market paper / web)

    Part III Technology

14. Search methods overview (slides / texts books in AI )
15. Evolutionary Computation (slides / background: Brabazon & O'Neill's book)
16. Constraint Satisfaction (slides / web / Module / background: Tsang's book)
17. Heuristic Search (background: Handbook of Metaheuristics)
18. Neural Network (background: Bishop's book)