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Introduction to Knowledge Representation

Introduction to Knowledge Representation

What Is Knowledge Representation?

Knowledge representation (KR) is the process of organizing information in a structured form that an AI system can use to answer questions, solve problems, or make decisions.

Humans often understand relationships naturally. For example, we know that a dog is an animal and that animals need food. An AI system needs information represented in a form it can process.

Knowledge representation helps convert information about the world into structures that computers can work with.

Why Does AI Need Knowledge Representation?

An AI system may need to answer questions such as:

  • Is a particular customer eligible for a refund?
  • Which products are related to a specific category?
  • What conclusion follows from a set of known facts?
  • Which action should be taken when certain conditions are met?

Without an appropriate representation, the system may struggle to connect facts or apply rules consistently.

Types of Knowledge in AI

1. Declarative Knowledge

Declarative knowledge describes facts about the world.

Examples:

  • Delhi is the capital of India.
  • A triangle has three sides.
  • A product's return period is 30 days.

2. Procedural Knowledge

Procedural knowledge describes how to perform a task.

Examples:

  • Steps to reset a password.
  • Instructions for processing an order.
  • A procedure for troubleshooting a network connection.

3. Heuristic Knowledge

Heuristic knowledge consists of practical rules of thumb that help solve problems efficiently.

For example, a troubleshooting system may suggest checking the network connection first when a website cannot be reached.

Heuristics are useful, but they do not guarantee a correct conclusion in every situation.

4. Relational Knowledge

Relational knowledge describes how entities are connected.

Examples:

  • A customer places an order.
  • A book has an author.
  • A city belongs to a country.

Common Knowledge Representation Techniques

Technique

Description

Example

Logical representationUses formal statements and rulesIf a user is verified, allow access
Semantic networksRepresent concepts as connected nodesDog → Animal
FramesOrganize information into structured objects with attributesProduct with name, price, and category
Production rulesRepresent knowledge as condition-action or condition-conclusion rulesIf stock is zero, mark item unavailable
Knowledge graphsRepresent entities and their relationships as a graphPerson → works for → Company

Each technique has different strengths. A system may use more than one representation depending on the task.

Example: Representing a Library

A library system might store the following facts:

  • Book: The Hobbit
  • Author: J. R. R. Tolkien
  • Category: Fantasy
  • Availability: Available

It can also store relationships such as:

  • The Hobbit was written by J. R. R. Tolkien.
  • The Hobbit belongs to the fantasy category.
  • The Hobbit is available for borrowing.

An AI system can use these facts to answer questions about the book or recommend related titles.

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Artificial Intelligence Tutorial

VA

Vishalini A

Artificial Intelligence Tutorial

MODULE 1 : AI Fundamentals and Real-World Problem Solving
lessonIntroduction to Artificial Intelligence
lessonHistory and Evolution of AI
lessonAI vs. Automation vs. Machine Learning vs. Deep Learning
lessonUnderstanding Generative AI
lessonAI Applications, Capabilities, and Limitations
lessonIdentifying AI Problems and Defining Success Criteria
MODULE 2 : Core AI Approaches and Intelligent Systems
lessonSymbolic and Rule-Based AI
lessonStatistical and Data-Driven AI
lessonHybrid AI Systems
lessonReactive and Goal-Driven AI Systems
lessonPredictive AI vs. Generative AI
lessonChoosing the Right AI Approach
MODULE 3 : Intelligent Agents and Environments
lessonUnderstanding Intelligent Agents
lessonSensors, Actuators, Percepts, and Actions
lessonThe PEAS Framework
lessonTypes of Intelligent Agents
lessonUnderstanding AI Environments
lessonBuilding a Simple Intelligent Agent
MODULE 4 : AI Problem-Solving and Search Algorithms
lessonProblem Formulation in AI
lessonUninformed Search Algorithms
lessonInformed Search and Heuristics
lessonImplementing A Search in Python
lessonComparing Search Algorithms and Their Applications
MODULE 5 : Knowledge Representation and Logical Reasoning
lessonIntroduction to Knowledge Representation
lessonFacts, Rules, and Semantic Networks
lessonPropositional Logic and Logical Operators
lessonFirst-Order Logic and Inference
lessonRule-Based Reasoning and Expert Systems
MODULE 6 : Mathematics and Probability for AI
lessonMathematical Foundations for AI
lessonLinear Algebra - Vectors, Matrices, and Dot Products
lessonStatistics and Descriptive Statistics
lessonProbability and Conditional Probability
lessonBayes' Theorem and Probabilistic Reasoning
lessonOptimization and Gradient Descent
MODULE 7 : Machine Learning Fundamentals
lessonIntroduction to Machine Learning
lessonTypes of Machine Learning
lessonUnderstanding Datasets, Features, and Labels
lessonCommon Machine Learning Algorithms
lessonBuilding Your First Machine Learning Model with Python
lessonEvaluating Machine Learning Models
MODULE 8 : Data Preparation and Feature Engineering
lessonUnderstanding Data Preparation
lessonHandling Missing Values and Duplicate Data
lessonEncoding Categorical Data
lessonFeature Scaling and Outlier Handling
lessonFeature Engineering and Feature Selection
lessonBuilding a Data Preprocessing Pipeline
MODULE 9 : Supervised Learning Algorithms
lessonUnderstanding Supervised Learning
lessonLinear Regression and Logistic Regression
lessonDecision Trees and Random Forests
lessonSupport Vector Machines and K-Nearest Neighbors
lessonEvaluating Supervised Learning Models
lessonHyperparameter Tuning and Model Selection
MODULE 10 : Unsupervised Learning Algorithms
lessonIntroduction to Unsupervised Learning
lessonClustering and the K-Means Algorithm
lessonHierarchical Clustering and DBSCAN
lessonDimensionality Reduction and Principal Component Analysis
lessonAnomaly Detection
lessonEvaluating and Applying Unsupervised Learning
MODULE 11 : Neural Networks and Deep Learning
lessonIntroduction to Neural Networks
lessonActivation Functions and Network Architecture
lessonHow Neural Networks Learn
lessonUnderstanding Deep Learning
lessonBuilding a Neural Network with Python
lessonOverfitting, Regularization, and Model Improvement
MODULE 12 : Natural Language Processing (NLP) and Computer Vision
lessonIntroduction to Natural Language Processing
lessonText Preprocessing and Representation
lessonCommon NLP Tasks and Sentiment Analysis
lessonIntroduction to Computer Vision
lessonImage Classification with Python
lessonReal-World Applications and Challenges
MODULE 13 : Generative AI Large Language Models (LLMs) and Prompt Engineering
lessonUnderstanding Generative AI
lessonLarge Language Models (LLMs)
lessonPrompt Engineering
lessonAdvanced Prompting and Reliable AI Outputs
lessonBuilding a Simple LLM Application with Python
lessonRetrieval-Augmented Generation and AI Agents
MODULE 14 : AI Model Evaluation Deployment and MLOps
lessonEvaluating AI Model Performance
lessonModel Deployment and AI APIs
lessonIntroduction to MLOps
lessonMonitoring, Data Drift, and Model Maintenance
lessonModel Optimization and Deployment Strategies
lessonThe End-to-End AI Lifecycle
MODULE 15 : Responsible AI Ethics Real-World Projects and Future Trends
lessonIntroduction to Responsible AI
lessonBias, Fairness, and Explainability
lessonPrivacy, Security, and AI Safety
lessonResponsible AI Governance and Regulations
lessonReal-World AI Projects and Responsible Implementation
lessonFuture Trends and Career Opportunities in AI

Learn via Video Course

Artificial Intelligence and Machine Learning Course Logo

Artificial Intelligence and Machine Learning Course

55 hrs

Get in Touch For Details

Artificial Intelligence Tutorial

MODULE 1 : AI Fundamentals and Real-World Problem Solving
lessonIntroduction to Artificial Intelligence
lessonHistory and Evolution of AI
lessonAI vs. Automation vs. Machine Learning vs. Deep Learning
lessonUnderstanding Generative AI
lessonAI Applications, Capabilities, and Limitations
lessonIdentifying AI Problems and Defining Success Criteria
MODULE 2 : Core AI Approaches and Intelligent Systems
lessonSymbolic and Rule-Based AI
lessonStatistical and Data-Driven AI
lessonHybrid AI Systems
lessonReactive and Goal-Driven AI Systems
lessonPredictive AI vs. Generative AI
lessonChoosing the Right AI Approach
MODULE 3 : Intelligent Agents and Environments
lessonUnderstanding Intelligent Agents
lessonSensors, Actuators, Percepts, and Actions
lessonThe PEAS Framework
lessonTypes of Intelligent Agents
lessonUnderstanding AI Environments
lessonBuilding a Simple Intelligent Agent
MODULE 4 : AI Problem-Solving and Search Algorithms
lessonProblem Formulation in AI
lessonUninformed Search Algorithms
lessonInformed Search and Heuristics
lessonImplementing A Search in Python
lessonComparing Search Algorithms and Their Applications
MODULE 5 : Knowledge Representation and Logical Reasoning
lessonIntroduction to Knowledge Representation
lessonFacts, Rules, and Semantic Networks
lessonPropositional Logic and Logical Operators
lessonFirst-Order Logic and Inference
lessonRule-Based Reasoning and Expert Systems
MODULE 6 : Mathematics and Probability for AI
lessonMathematical Foundations for AI
lessonLinear Algebra - Vectors, Matrices, and Dot Products
lessonStatistics and Descriptive Statistics
lessonProbability and Conditional Probability
lessonBayes' Theorem and Probabilistic Reasoning
lessonOptimization and Gradient Descent
MODULE 7 : Machine Learning Fundamentals
lessonIntroduction to Machine Learning
lessonTypes of Machine Learning
lessonUnderstanding Datasets, Features, and Labels
lessonCommon Machine Learning Algorithms
lessonBuilding Your First Machine Learning Model with Python
lessonEvaluating Machine Learning Models
MODULE 8 : Data Preparation and Feature Engineering
lessonUnderstanding Data Preparation
lessonHandling Missing Values and Duplicate Data
lessonEncoding Categorical Data
lessonFeature Scaling and Outlier Handling
lessonFeature Engineering and Feature Selection
lessonBuilding a Data Preprocessing Pipeline
MODULE 9 : Supervised Learning Algorithms
lessonUnderstanding Supervised Learning
lessonLinear Regression and Logistic Regression
lessonDecision Trees and Random Forests
lessonSupport Vector Machines and K-Nearest Neighbors
lessonEvaluating Supervised Learning Models
lessonHyperparameter Tuning and Model Selection
MODULE 10 : Unsupervised Learning Algorithms
lessonIntroduction to Unsupervised Learning
lessonClustering and the K-Means Algorithm
lessonHierarchical Clustering and DBSCAN
lessonDimensionality Reduction and Principal Component Analysis
lessonAnomaly Detection
lessonEvaluating and Applying Unsupervised Learning
MODULE 11 : Neural Networks and Deep Learning
lessonIntroduction to Neural Networks
lessonActivation Functions and Network Architecture
lessonHow Neural Networks Learn
lessonUnderstanding Deep Learning
lessonBuilding a Neural Network with Python
lessonOverfitting, Regularization, and Model Improvement
MODULE 12 : Natural Language Processing (NLP) and Computer Vision
lessonIntroduction to Natural Language Processing
lessonText Preprocessing and Representation
lessonCommon NLP Tasks and Sentiment Analysis
lessonIntroduction to Computer Vision
lessonImage Classification with Python
lessonReal-World Applications and Challenges
MODULE 13 : Generative AI Large Language Models (LLMs) and Prompt Engineering
lessonUnderstanding Generative AI
lessonLarge Language Models (LLMs)
lessonPrompt Engineering
lessonAdvanced Prompting and Reliable AI Outputs
lessonBuilding a Simple LLM Application with Python
lessonRetrieval-Augmented Generation and AI Agents
MODULE 14 : AI Model Evaluation Deployment and MLOps
lessonEvaluating AI Model Performance
lessonModel Deployment and AI APIs
lessonIntroduction to MLOps
lessonMonitoring, Data Drift, and Model Maintenance
lessonModel Optimization and Deployment Strategies
lessonThe End-to-End AI Lifecycle
MODULE 15 : Responsible AI Ethics Real-World Projects and Future Trends
lessonIntroduction to Responsible AI
lessonBias, Fairness, and Explainability
lessonPrivacy, Security, and AI Safety
lessonResponsible AI Governance and Regulations
lessonReal-World AI Projects and Responsible Implementation
lessonFuture Trends and Career Opportunities in AI

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HCL GUVI (Grab Your Vernacular Imprint) Geek Network Private Limited is a leading online learning and skills development company, incubated by IIT Madras and IIM Ahmedabad.

Established in 2014 and acquired by the HCL Group in 2022, HCL GUVI is dedicated to providing effective and high-quality learning and skilling programs that transcend language barriers in technology education. HCL GUVI today is trusted by over 4.8 million users, 20 languages and 2500+ corporate partners.

app storeplay store
Refer & Earn

HCL GUVI (Grab Your Vernacular Imprint) Geek Network Private Limited is a leading online learning and skills development company, incubated by IIT Madras and IIM Ahmedabad.

Established in 2014 and acquired by the HCL Group in 2022, HCL GUVI is dedicated to providing effective and high-quality learning and skilling programs that transcend language barriers in technology education. HCL GUVI today is trusted by over 4.8 million users, 20 languages and 2500+ corporate partners.

app storeplay storeRefer & Earn
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