History, linguistics, facts and agents of artificial-intelligence.
Intelligent Agents:
Various aspects of intelligent agents like their architecture and environments.
Problem Solving:
Different approaches to problem solving like uninformed and informed search strategies, local search and optimization problems and other constraints satisfaction problems.
Adversarial Search:
Approaches to game theory problems like state space search and alpha beta pruning.
Logical Agents:
Agents that reason logically, first order logic and the inference in first order logic which includes forward and backward reasoning.
Knowledge and Reasoning:
Inference in first order logic, rule based system, semantic net, frames, unification and lifting.
Planning and Acting in the Real World:
Different types of planning like partial order planning, graph planning and real world planning.
Uncertain Knowledge and Reasoning:
Uncertainty, probability notations and bayesian networks and various probabilistic reasoning systems. Hidden markov models, expert systems, Uncertain reasoning also includes semantic representation and object recognition.
Learning:
Learning from observations which includes decision trees, learning in neural and belief networks, reinforcement learning and knowledge in learning. It includes questions on inductive logic programming.
Communicating, Perceiving and Acting:
The agents that communicate which includes robotics, practical natural language processing and perceptions.
LISP Programming:
LISP programming.
AI Algorithms & Statistics:
Various other topics of artificial-intelligence, ai algorithms and statistics.