The course introduces the variety of concepts in the field of artificial intelligence. It discusses the philosophy of AI, and how to model a new problem as an AI problem. It describes a variety of models such as search, logic, Bayes nets, and MDPs, which can be used to model a new problem. It also teaches many first algorithms to solve each formulation. The course prepares a student to take a variety of focused, advanced courses in various subfields of AI.
INTENDED AUDIENCE : Undergraduate students in computer sciencePREREQUISITES : Data Structures, ProbabilityINDUSTRY SUPPORT : Most software companies
Week 1 :Introduction: Philosophy of AI, Definitions Week 2 :Modeling a Problem as Search Problem, Uninformed Search Week 3:Heuristic Search, Domain Relaxations Week 4 :Local Search, Genetic Algorithms Week 5 :Adversarial Search Week 6 :Constraint Satisfaction Week 7 :Propositional Logic & Satisfiability Week 8 :Uncertainty in AI, Bayesian Networks Week 9 :Bayesian Networks Learning & Inference, Decision Theory Week 10:Markov Decision Processes Week 11:Reinforcement Learning Week 12:Introduction to Deep Learning & Deep RL