3 Credits

Sem 7

Course code: CACS410

Artificial Intelligence

AI concepts, search algorithms, knowledge representation, and machine learning basics.

Also in this semester:

Cyber Law and Professional Ethics Cloud Computing Internship Image Processing Database Administration Network Administration Software Project Management Advanced Dot Net Technology

Syllabus Questions

Course Syllabus

1 Doc

8 views6 Units48 Teaching Hrs

Course Information

Type: Theory
Course Code: CACS410
Credit Hours: 3
Full Marks: 100
Pass Marks: 40

Course Description

The course introduces basics of artificial intelligence. It covers fundamental concepts such as artificial intelligence, problem solving, knowledge representation, neural networks, machine learning, natural language processing, machine vision and expert systems.

Course Units

6 Units · 48 Hrs

1.1 Intelligence, Intelligent behavior, Artificial Intelligence, Understanding Al based on thought process and behavior, Hard vs. Strong Al, Soft vs. Weak AI
1 .2 Foundations of Al
1 .3 Applications of Al
1.4 Intelligent Agents: Introduction of agents, Structure of Intelligent agent, Properties of Intelligent Agents, PEAS description of Agents, Types of Agents: Simple Reflexive, Model Based, Goal Based, Utility Based, Learning agent, Environment Types: Deterministic, Stochastic, Static, Dynamic, Observable, Semi-observable, Single Agent, Multi Agent

2.1 Definition of a Problem, Problem as a state space representation, Problem formulation, Well-defined problems, Constraint satisfaction problem, Water jug problem-Queen problem, Cryptarithmetic problem, Graph coloring problem
2.2 Problem solving by searching, types of searching, measuring problem solving performance, General State Space Search
2.3 Uninformed: Breadth-First Search, Depth-First Search, Depth-Limited Search, Iterative Deepening depth first Search, Bidirectional Search, using uninformed search techniques for solving N-Queens Problem, Puzzle problem etc.
2.4 Informed search: Greedy Best-First Search, A* Search, Optimality of A*, Local search: Hill Climbing, Simulated Annealing, using informed search techniques for solving N-Queens Problem, Puzzle problem etc.
2.5 Game Playing, Optimal Decisions in Games, Alpha — Beta Pruning, Minimax Algorithm, Tic-Tac —Toe Problem, Stochastic Games

3.1 Definition and importance of Knowledge, Issues in Knowledge Representation, Knowledge Representation Systems, Properties of Knowledge Representation Systems, Types of Knowledge, The Role of Knowledge
3.2 Knowledge representation techniques: Rule Based, Semantic Nets, Frames, Logic based
3.3 Propositional Logic, Syntax and Semantic of propositional logic, Proof by Resolution, Conjunctive Normal Form (CNF), Resolution Algorithm, Limitations of Propositional Logic, Forward and Backward Chaining
3.4 Predicate Logic, FOPL, Syntax, Semantics, Quantification, horn clauses, Inference with FOPL: By converting into PL (Existential and universal instantiation), Rules of inference, Unification and lifting, CNF for FOP L, Inference using resolution, Resolution Refutation System (RRS)
3.5 Handling Uncertain Knowledge, Random Variables, Prior and Posterior Probability, Inference using Full Joint Distribution, Bayes r Rule and its use, Bayesian Networks, Reasoning in Bayesian Networks

4.1 Concepts of machine learning
4.2 Rote learning, learning by analogy, inductive learning, Explanation based learning, Supervised and unsupervised learning, learning by evolution (genetic algorithm)

5.1 Introduction to artificial neural network, Mathematical model of neural network, types of neural network: feed-forward, feed-back, Gate realization using neural network, Learning in neural networks: Back propagation algorithm, Hopfield network, Boltzmann machines
5.2 Concepts of natural language understanding and natural language generation, Steps in natural language processing, Syntax analysis, Semantic analysis, Pragmatic analysis

6.1 Expert System, Architecture of an expert system, Stages of expert systems development.
6.2 Concept of Machine Vision. Steps of machine vision, application of machine

Laboratory Works

Laboratory exercises can be conducted in LISP, PROLOG or any other high level programming language. Laboratory exercises must cover the concepts of rule based intelligent agents, inference and reasoning, search techniques, neural networks, etc. For solving practical problems.

References & Books

Stuart Russel and Peter Norvig, Artificial Intelligence A Modern Approach, Pearson
E. Rich, K. Knight, Shivashankar B. Nair, Artificial Intelligence, Tata McGraw Hill.
George F. Luger, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Benjamin/Cummings Publication
D. W. Patterson, Artificial Intelligence and Expert Systems, Prentice Hall.
P. H. Winston, Artificial Intelligence, Addison Wesley.

Course Syllabus

1 Doc

8 views6 Units48 Teaching Hrs

Course Information

Type: Theory
Course Code: CACS410
Credit Hours: 3
Full Marks: 100
Pass Marks: 40

Course Description

Course Units

6 Units · 48 Hrs

1.1 Intelligence, Intelligent behavior, Artificial Intelligence, Understanding Al based on thought process and behavior, Hard vs. Strong Al, Soft vs. Weak AI
1 .2 Foundations of Al
1 .3 Applications of Al
1.4 Intelligent Agents: Introduction of agents, Structure of Intelligent agent, Properties of Intelligent Agents, PEAS description of Agents, Types of Agents: Simple Reflexive, Model Based, Goal Based, Utility Based, Learning agent, Environment Types: Deterministic, Stochastic, Static, Dynamic, Observable, Semi-observable, Single Agent, Multi Agent

2.1 Definition of a Problem, Problem as a state space representation, Problem formulation, Well-defined problems, Constraint satisfaction problem, Water jug problem-Queen problem, Cryptarithmetic problem, Graph coloring problem
2.2 Problem solving by searching, types of searching, measuring problem solving performance, General State Space Search
2.3 Uninformed: Breadth-First Search, Depth-First Search, Depth-Limited Search, Iterative Deepening depth first Search, Bidirectional Search, using uninformed search techniques for solving N-Queens Problem, Puzzle problem etc.
2.4 Informed search: Greedy Best-First Search, A* Search, Optimality of A*, Local search: Hill Climbing, Simulated Annealing, using informed search techniques for solving N-Queens Problem, Puzzle problem etc.
2.5 Game Playing, Optimal Decisions in Games, Alpha — Beta Pruning, Minimax Algorithm, Tic-Tac —Toe Problem, Stochastic Games

3.1 Definition and importance of Knowledge, Issues in Knowledge Representation, Knowledge Representation Systems, Properties of Knowledge Representation Systems, Types of Knowledge, The Role of Knowledge
3.2 Knowledge representation techniques: Rule Based, Semantic Nets, Frames, Logic based
3.3 Propositional Logic, Syntax and Semantic of propositional logic, Proof by Resolution, Conjunctive Normal Form (CNF), Resolution Algorithm, Limitations of Propositional Logic, Forward and Backward Chaining
3.4 Predicate Logic, FOPL, Syntax, Semantics, Quantification, horn clauses, Inference with FOPL: By converting into PL (Existential and universal instantiation), Rules of inference, Unification and lifting, CNF for FOP L, Inference using resolution, Resolution Refutation System (RRS)
3.5 Handling Uncertain Knowledge, Random Variables, Prior and Posterior Probability, Inference using Full Joint Distribution, Bayes r Rule and its use, Bayesian Networks, Reasoning in Bayesian Networks

4.1 Concepts of machine learning
4.2 Rote learning, learning by analogy, inductive learning, Explanation based learning, Supervised and unsupervised learning, learning by evolution (genetic algorithm)

5.1 Introduction to artificial neural network, Mathematical model of neural network, types of neural network: feed-forward, feed-back, Gate realization using neural network, Learning in neural networks: Back propagation algorithm, Hopfield network, Boltzmann machines
5.2 Concepts of natural language understanding and natural language generation, Steps in natural language processing, Syntax analysis, Semantic analysis, Pragmatic analysis

6.1 Expert System, Architecture of an expert system, Stages of expert systems development.
6.2 Concept of Machine Vision. Steps of machine vision, application of machine

Laboratory Works

References & Books

Stuart Russel and Peter Norvig, Artificial Intelligence A Modern Approach, Pearson
E. Rich, K. Knight, Shivashankar B. Nair, Artificial Intelligence, Tata McGraw Hill.
George F. Luger, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Benjamin/Cummings Publication
D. W. Patterson, Artificial Intelligence and Expert Systems, Prentice Hall.
P. H. Winston, Artificial Intelligence, Addison Wesley.

Artificial Intelligence

Artificial Intelligence

Course Syllabus

Course Information

Course Description

Course Units

Introduction

Problem Solving Methods

Knowledge Representation And Reasoning

Learning

Neural Networks And Natural Language Processing

Expert System And Machine Vision

Laboratory Works

References & Books

Course Syllabus

Course Information

Course Description

Course Units

Introduction

Problem Solving Methods

Knowledge Representation And Reasoning

Learning

Neural Networks And Natural Language Processing

Expert System And Machine Vision

Laboratory Works

References & Books