Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. AI-based products and services rely on large amounts of high-quality labeled data for training, tuning, and evaluating machine learning algorithms.
Mode Of Examination
Number Of Question: 100 (1 Marks Each)
Total Time: 120 Min
Introduction to Artificial Intelligence:
History, linguistics, facts and agents of artificial-intelligence.
Various aspects of intelligent agents like their architecture and environments.
Different approaches to problem solving like uninformed and informed search strategies, local search and optimization problems and other constraints satisfaction problems.
Approaches to game theory problems like state space search and alpha beta pruning.
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 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.
AI Algorithms & Statistics:
Various other topics of artificial-intelligence, ai algorithms and statistics.