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Artificial Intelligence

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Specific References

M.Sc. (Computer Science)-I  SPPU Course code: CS 502 MJ

M.Sc. (Data Science)-I    SPPU Course code: DS 512 MJ

M.Sc. (Information Technology)-I    SPPU Course code: IT 551 MJ

A Text book of

Artificial Intelligence

Author: Dr. Harshita Vachhani

ISBN: 978-93-94022-48-5

Quantity
In Stock

Contents

1. Introduction to Artificial Intelligence

1. What is Artificial Intelligence

2. Forms of AI

3. Purpose of AI / Goals of AI

4. Scope and Applications of AI

5. History of Artificial Intelligence    

6. Ethical Considerations in AI Development

7. What is Data Science

8. Artificial Intelligence in Data Science

9. Role of Artificial Intelligence in Data Science

10. Comparison of AI and Data Science

11. Advantages of Artificial Intelligence

12. Disadvantages of Artificial Intelligence

2. Foundations of Artificial Intelligence

1. Risks and Benefits in AI

2. Characteristics of Intelligent Agents

3. Structure of Agents

4. Agents and Environments

5. Types of Intelligent Agents

3. Problem Solving     

1. Define Problems, Problem Spaces and Search

1.1 Problems, Problem Spaces and Search

1.2 AI - General Problem Solving

2. Problem Solving Methods/Techniques

3. Problem Solving Agents

4. Search Algorithm Terminologies

5. Properties of Search Algorithm

6. Defining Problem as a State Space Search

7. Production Systems

7.1 Problem Characteristics

7.2 Characteristics of Production Systems

8. Uninformed Search Methods

8.1 Breadth First Search (BFS)

8.2 Depth-First Search

8.3 Bounded Depth-First Search

8.4 Depth Limited Search

8.5 Depth First Iterative Deepening(DFID)

9. Informed Search Methods: Greedy best first Search, A* Search, Memory

9.1 Best First Search Algorithm (Greedy Search)

9.2 A* Search Algorithm

9.3 Memory Bounded Heuristic Search

10. AO* Search Algorithm

11. Local Search Algorithms and Optimization Problems: Hill Climbing

11.1 Hill Climbing Search

11.2 Simulated Annealing

11.3 Local Beam Search

11.4 Genetic Algorithms

12. Adversarial Search: Games, Optimal Strategies, the Minimax

12.1 Game

12.2 Mini-Max Algorithm in Artificial Intelligence

12.3 Alpha-Beta Pruning

4. Game Theory

1. Optimal Decisions in Game Theory

2. Heuristic Alpha–Beta Tree Search

3. Monte Carlo Tree Search(MCTS)

4. Stochastic Games

5. Partially Observable Games

6. Limitations of Game Search Algorithms

5. Constraint Satisfaction Problems(CSP)

1. Introduction to CSP

1.1 Constraint Satisfaction Problem(CSP)

2. Knowledge Representation

3. Representations and Mappings

4. Approaches to Knowledge Representations

4.1 Inheritable Knowledge

4.2 Inferential Knowledge

4.3 Procedural Knowledge

5. Knowledge Representation Method 

6. Logical Agents

6.1 Knowledge-Based Agents

7. Propositional Logic

8. Language

8.1 Syntax of Propositional Logic

8.2 Logical Connectives

8.3 Truth Table

9. Logical(Semantic) Equivalence

10. Rules of Inference and Natural Deduction

10.1  Inference  

10.2  Types of Inference Rules

11. Axiomatic Systems and Hibert Style Proofs

12. The Tableau Method

13. The Resolution Refutation Method

6. Reasoning

1. Introduction to Reasoning

2. Inference in First-Order Logic

3. Existential Instantiation

4. Existential Introduction

5. Propositional vs. First-Order Inference

6. Unification and First-Order Inference

7. Forward Chaining, Backward Chaining

8. Categories and Objects

9. Mental Objects and Modal Logic

10. Reasoning Systems for Categories

11. Reasoning with Default Information

7. Planning

1. Introduction to Planning

2. A Simple Planning Agent

3. Planning in State Space Search

4. Classical Planning

5. Heuristics for Planning

6. Automated Planning

7. Various Planning Techniques

7.1 Partial Order Planning in AI

7.2 Hierarchical Planning in AI

7.3 Conditional Planning in AI

8. Recent Trends in AI

1. Applications of AI

2. Language Models

3. Information Retrieval

4. Information Extraction

5. Introduction to Natural Language Processing(NLP)

6. Reinforcement Learning and Robotics

7. Computer Vision Breakthroughs

8. AI in Healthcare

9. AI in Finance

9.1 Autonomous Systems

9.2 Introduction to Explainable AI

9.2 Generative AI

10. Generative AI: Benefits, Use Cases, and Examples

9. Intelligent System

1. What is an Intelligent Agent in AI

2. Task Environment of Agents

3. Classification of Agents

3.1 Types of Agents

4. Architecture of Agents

5. Properties of Intelligent Agents

6. Examples of Intelligent Agents

7. AI Problems(State Space Search)

10. Knowledge Representation

1. Knowledge Representation and Need of Knowledge Representation

2. Knowledge Representation and Mapping Schemes

3. Properties of Good Knowledge Based System

3.1 Types of Knowledge

4. Knowledge Representation Issues

5. Logic Representation

5.1 AND-OR Graph

6. The Wumpus World, The Propositional logic

6.1 Exploring the Wumpus World

6.2 Knowledge-Base for Wumpus World

7. First Order Logic: Syntax and Semantic, Inference in FOL

7.1 Knowledge Engineering in First-order Logic

8. Forward and Backward Chaining

11. Machine Learning Fundamentals

1. Introduction to Machine Learning

2. Classification of Machine Learning/Forms of Machine Learning

3. Classification Algorithms

4. Decision Tree

5. k-Nearest Neighbors(k-NN)

6. Model Evaluation and Cross-Validation in Machine Learning

12. Neural Networks and Deep Learning

1. Introduction to Neural Networks and Activation Functions

1.1 Neural Network

1.2 GELU(Gaussian Error Linear Units)

2. Training Neural Networks: Backpropagation, Optimization Techniques

3. Feed Forward Network

4. Convolutional Neural Networks(CNNs) for Computer Vision

13. Natural Language Processing(NLP)

1. Introduction

2. Text Pre-processing and Tokenization

3. Word Embeddings: Word2Vec, GloVe

4. Sentiment Analysis and Text Classification

4.1 Text Classification

14. Reinforcement Learning

1. Introduction

1.1 Markov Decision Processes(MDPs)

1.2 Dynamic Programming

2. Q-learning and Temporal Difference(TD) Learning

3. Deep Q Networks(DQNs) for Deep RL

15. AI Ethics and Future Trends

1. Ethical Considerations in AI Development and Deployment

2. AI Safety and Bias Mitigation

3. Emerging Trends in AI Research and Applications

16. AI Project

1. Key Components and Steps for a Project

2. Project: Image Classification with Convolutional Neural Networks(CNN)

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