Course module

Paper 2 | Unit 10 | Artificial Intelligence — UGC NET Computer Science (Paper 1 + Paper 2)

Explore the module outline and lessons below. Enroll free to begin studying here.

Lessons: 239
Duration: 18 hr 42 min
PYQs: 212
Practice: 256

6% of this course's PYQ weight is covered in this module.

Syllabus index

Lessons in this module

239 linked lessons

NET - Unit Test - AI - Level 2Test

Previous-year questions

Explore the course

Lessons in this module

Approaches to AI

Foundations and Evolution of AI

Philosophical and Theoretical Approaches to AI

Problem Representation in AI

Search Strategies in AI

Search Algorithms

Uninformed Search Strategies

Fundamentals of Heuristic Search

Best First Search

A* Search Algorithm

AO* and Branch-Based Search

Local Search Fundamentals

Advanced Local Optimization Techniques

Means–Ends Analysis

Game Playing

Introduction of Game Playing

Puzzle-Based State Space Search

Minimax Search Algorithm

Alpha-Beta Pruning Technique

Constraint Satisfaction Problem

Knowledge Representation

Foundations of Knowledge Representation

Logical Reasoning Principles

Normal Forms and Logical Transformation

Inference Mechanisms in First Order Logic

Structured Knowledge Representation Models

Script-Based Knowledge Representation

Expert Systems and Uncertainty

Planning

Fundamentals of Planning

State-Space Planning Approaches

Non-Linear and Plan-Space Planning

Planning Challenges and Anomalies

Hierarchical Planning Techniques

Multi Agent Systems

Foundations of Intelligent and Rational Agents

Task Environment Classification

Environment Complexity Dimensions

Agent Architecture and Program Types

Knowledge Sharing and Coordination

Fuzzy Sets

Fundamentals of Fuzzy Sets

Definitions and Operations

Fuzzy Arithmetic and Membership Functions

T-Norms and S-Norms

Fuzzy Relations and Composition

Fuzzy Systems and Inference Models

Natural Language Processing

Introduction to Natural Language Processing

NLP Processing Stages

Discourse and Context Integration

Artificial Neural Networks

Introduction to Artificial Neural Networks

Perceptron and Activation Functions

Multi-Layer Perceptron and Backpropagation

Neural Network Architectures

Training of Hopfield Networks

Unsupervised Learning and Clustering

Self-Organizing Maps (SOM)

Genetic Algorithms

Fundamentals of Genetic Algorithms

Genetic Operators: Crossover and Mutation

Genetic Representation and Encoding

Module lessons

Lessons

Previous-year questions

Explore the course