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
An Introduction to Artificial Intelligence
Indian Institute of Technology Delhi and NPTEL via Swayam
This course may be unavailable.
Overview
Syllabus
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
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
Taught by
Prof. Mausam
