Course Overview
The course M.C.A. SEM V MCA 52: Artificial Intelligence aims to provide a comprehensive introduction to the concepts, techniques, and applications of AI. The course covers the following topics:
Introduction to AI: The foundations, history, and state of the art of AI; the goals and approaches of AI; the techniques and branches of AI; the applications and challenges of AI.
Intelligent Agents: The concept and structure of intelligent agents; how agents should act; agents that reason logically; a knowledge-based agent; the Wumpus World environment.
Search and Control Strategies: The problem-solving and search concepts; the performance and complexity of algorithms; the tree structure and search space; the search notations and heuristic functions; the strategies for search; forward and backward chaining.
Knowledge Representation: The concept and importance of knowledge representation; the representation, reasoning, and logic concepts; propositional logic; first-order logic; inference rules and methods; resolution and unification.
Reasoning System: The concept and types of reasoning systems; deductive reasoning; inductive reasoning; abductive reasoning; non-monotonic reasoning; probabilistic reasoning.
Game Theory: The concept and applications of game theory; games with perfect information; games with imperfect information; minimax algorithm; alpha-beta pruning; expectimax algorithm.
Learning System: The concept and types of learning systems; supervised learning; unsupervised learning; reinforcement learning; explanation-based learning; learning using relevance information; inductive logic programming.
Expert Systems: The concept and components of expert systems; knowledge base; inference engine; user interface; knowledge acquisition; knowledge engineering; examples of expert systems.
Neural Networks: The concept and structure of neural networks; artificial neurons and activation functions; feedforward neural networks; backpropagation algorithm; recurrent neural networks; convolutional neural networks.
Fundamentals of Genetic Algorithms: The concept and principles of genetic algorithms; biological background and terminology; genetic operators and parameters; fitness function and selection methods; crossover and mutation methods.
Natural Language Processing: The concept and applications of natural language processing (NLP); the levels of NLP analysis; morphology and syntax analysis; semantics and pragmatics analysis; NLP tasks and techniques.
Common Sense: The concept and challenges of common sense reasoning; the sources and representation of common sense knowledge; the methods and systems for common sense reasoning.
Course Resources
The course M.C.A. SEM V MCA 52: Artificial Intelligence is based on the following books:
[Artificial Intelligence: A Modern Approach] by Stuart Russell and Peter Norvig. This book is one of the most popular and comprehensive textbooks on AI, covering both the theoretical foundations and practical applications of AI.
[Artificial Intelligence PDF Notes, Syllabus, Book [2021]] by Geektonight. This is an online resource that provides a complete artificial intelligence notes pdf, syllabus, book, question paper, interview questions, etc. for B Tech, BCA, MCA students.
The course also requires students to perform lab exercises using various tools and platforms for AI development, such as Python, Prolog, TensorFlow, etc. Some of the lab manuals are available online:
[Artificial Intelligence Lab] by Mumbai University. This lab manual contains 10 experiments on topics such as search strategies, knowledge representation, expert systems, neural networks, genetic algorithms, etc.
[MCA-401 Artificial Intelligence & Applications] by Rajiv Gandhi Proudyogiki Vishwavidyalaya. This lab manual contains 12 experiments on topics such as intelligent agents, game theory, learning systems, natural language processing, etc.
Course Assessment
The course M.C.A. SEM V MCA 52: Artificial Intelligence is assessed by a combination of internal and external evaluation. The internal evaluation consists of assignments, quizzes, lab work, and mid-term tests. The external evaluation consists of a final theory exam and a final practical exam. The weightage of each component may vary depending on the university or college.
Course Outcomes
By the end of the course M.C.A. SEM V MCA 52: Artificial Intelligence, students should be able to:
Understand the basic concepts, techniques, and applications of AI.
Design and implement intelligent agents that can act rationally in various environments.
Apply different search and control strategies to solve problems efficiently and effectively.
Use various knowledge representation and reasoning methods to model and manipulate knowledge.
Analyze and compare different game theory algorithms for optimal decision making.
Implement different learning systems that can acquire and improve knowledge from data and experience.
Develop and evaluate expert systems that can provide expert advice and solutions.
Build and train neural networks that can perform complex tasks such as classification, regression, etc.
Use genetic algorithms to optimize solutions for various problems.
Process and understand natural language using various NLP techniques.
Perform common sense reasoning using various sources and methods of common sense knowledge.
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