Week 1: Motivating Applications of AI/ML in Economics & Politics. Basic ideas of AI/ML, formulating / deciphering real life problems using these techniques. - DB
Week 2: Optimization and Search techniques (unconstrained and constrained optimization, concept of pareto-optimality, heuristic search, game tree) - AM
Week 3: Basic Predictive Algorithms (Linear Regression, Decision Trees, Random Forests, Bayesian classifier), Neural Networks, Time Series Prediction - AM
Week 4: Causality and Attribution (Shapley value analysis of predictive models, Granger Causality, Causal Graphical Models and do-Calculus, Randomized Control Trials) - AM Put-call, Hedging - DB
Week 5: Introduction to Game Theory (Cooperative and noncooperative Game Theory, dilemma problems), Bayesian Games, Mechanism Design with Economics applications - DB
Week 6: Auction Theory (Vickrey, Myerson Auctions), Case studies of auctions, advertising strategies on the internet - PD
Week 7: Case Studies: i) Learning Theory for Economics ii) Customer Behavior Analysis for Recommender Systems - PD(i) DB(ii)
Week 8: Case studies: i) Reinforcement Learning in Finance, ii) Multi-agent simulation of economic systems, Econo-physics - AM
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