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Artificial Intelligence

Artificial Intelligence focuses on creating systems that can simulate human intelligence. It covers problem-solving, knowledge representation, search techniques, reasoning, and learning methods used to build intelligent agents and automated decision-making systems.

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TabFlux . Artificial Intelligence . TU . BDS

Artificial Intelligence

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Course Title: Artificial Intelligence

Course No: BDS251

Nature of the Course: Theory + Lab

Semester: 4

Full Marks: 45 + 30

Pass Marks: 18 + 12

Credit Hours: 3

Course Description

Course Objectives

Course Contents

1. Introduction

6 hrs

1.1. Artificial Intelligence (AI)

1.2. AI Dimensions: Acting Humanly, Thinking Humanly, Acting Rationally, Thinking Rationally

1.3. Foundations of AI

1.4. History of AI

1.5. Agents; Rational Agents

1.6. PEAS Description

1.7. Environment Types

1.8. Agents Types: Simple Reflex, Model Based, Goal Based, Utility Based, Learning Agent

2. Problem Solving by Searching

11 hrs

2.1. Problem Solving Agents

2.2. Search Problems and Solutions

2.3. State Space Representation

2.4. Problem Formulation

2.5. Solving Problems by Searching

2.6. Search Algorithms

2.7. Measuring Problem Solving Performance

2.8. Uninformed Search Strategies: Breadth First Search, Depth First Search, Depth Limited Search, Iterative Deepening Search, Bidirectional Search

2.9. Informed Search Strategies: Greedy Best First Search, A* Search

2.10. Local Search Strategies: Hill Climbing Search, Simulated Annealing

2.11. Adversarial Search

2.12. Mini-Max Search, Alpha-Beta Pruning

2.13. Constraint Satisfaction Problems

2.14. Examples of Constraint Satisfaction Problems

3. Knowledge Representation and Reasoning

10 hrs

3.1. Knowledge and Knowledge Representation

3.2. Knowledge Representation Systems

3.3. Logic Based Knowledge Representation: Propositional and Predicate

3.4. Propositional Logic: CNF Form

3.5. Resolution Algorithm in Propositional Logic

3.6. Predicate Logic: FOPL

3.7. Quantifiers in FOPL

3.8. Reasoning in FOPL: Unification and Lifting

3.9. Inference using Resolution in FOPL

3.10. Semantic Nets, Frames, Rule Based Systems

3.11. Uncertain Knowledge, Prior and Posterior Probability

3.12. Inference using Full Joint Distributions

3.13. Bayes' Rule

3.14. Bayesian Networks

4. Machine Learning

6 hrs

4.1. Machine Learning

4.2. Supervised Learning

4.3. Unsupervised Learning

4.4. Reinforcement Learning

4.5. Artificial Neural Network (ANN)

4.6. Learning with Genetic Algorithms

5. Applications of AI

7 hrs

5.1. Expert Systems

5.2. Architecture of Expert System

5.3. Natural Language Processing

5.4. Steps of Natural Language Processing

5.6. Robot Hardware

5.7. Robotic Perception

5.8. Planning and Control

5.9. Computer Vision

5.10. Components of Computer Vision

3 hrs

6.1. Ethics of AI

6.2. Lethal Autonomous Weapons

6.3. Surveillance, Security, and Privacy

6.4. Fairness and Bias

6.5. Trust and Transparency

6.6. Future of Work

6.7. Robot Rights

7. Advanced Concepts in AI

5 hrs

7.1. Deep learning

7.2. Generative AI

7.3. Explainable AI

7.4. Multimodal AI

7.5. Generative Adversarial Networks

7.6. Large Language Models

7.7. Prompt Engineering

7.8. Quantum AI

Laboratory Works

1.Laboratory Works

Text Books

1.Stuart Russel and Peter Norvig (2022). Artificial Intelligence A Modern Approach, Pearson

Reference Books

1.Kevin Knight, Elaine Rich and Shivashankar B. Nair (2017). Artificial Intelligence, Tata McGraw Hill
2.Rajiv Chopra, Artificial Intelligence, S Chand Publication
3.Gaurav Leekha (2021). Learn AI with Python, BPB Publication
4.George F. Luger, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Benjamin/Cummings Publication
5.D. W. Patterson, Artificial Intelligence and Expert Systems, Prentice Hall
6.P. H. Winston, Artificial Intelligence, Addison Wesley

Notes:

Source:

The course introduces the fundamental concepts of artificial intelligence. It includes the basics of artificial intelligence, intelligent agents, problem solving using searching, informed and uninformed search techniques, adversarial searching, knowledge representation systems, machine learning, expert system, natural language processing, computer vision, AI ethics and advanced concepts in AI.

Upon successful completion of this course, students will be able to: • Acquaint with the concepts of artificial intelligence • Configure intelligent agents • Solve problems using search techniques • Represent knowledge in AI systems • Understand the basics of machine learning • Design expert systems • Perform basic operations of NLP • Understand concept of computer vision • Know about the AI ethics • Get familiar with advances in AI

The laboratory work consists of implementation of Intelligent Agents, Constraint Satisfaction Problems, Blind and Heuristic Search Algorithms, Knowledge Representation Systems, Artificial Neural Networks, Expert Systems, Basics of Natural Language Processing.

This syllabus follows the official Bachelor in Data Science curriculum of Tribhuvan University. In case of any doubt or revision, the university's published syllabus shall be considered authoritative.