Fundamentals of Artificial Intelligence and Applications (Course Code: BCS111)
Course Type: Minor
Credits: 4
Course Overview
The Fundamentals of Artificial Intelligence and Applications course introduces students to the essential principles, history, and techniques of Artificial Intelligence (AI). It focuses on intelligent agents, search strategies, knowledge representation, and real-world AI applications.
Course Objectives
- Understand the fundamental concepts and history of Artificial Intelligence.
- Explore various types of intelligent agents and their architectures.
- Learn different search strategies used in AI problem-solving.
- Gain insights into methods of knowledge representation.
- Explore practical applications of AI in multiple domains.
Learning Outcomes
After completing this course, students will be able to:
- Recall the foundational concepts and historical development of AI.
- Understand the principles behind intelligent agent design.
- Apply uninformed and informed search strategies to solve AI problems.
- Analyze various approaches to knowledge representation and reasoning.
- Evaluate different real-world applications of Artificial Intelligence.
Course Outline
Unit I: Introduction to Artificial Intelligence
Introduction, brief history of AI, types of intelligent systems, and categorization of AI programs. It covers the foundations and sub-areas of AI, major applications, development of AI programming languages, and current and future trends in Artificial Intelligence.
📘 Unit 1 Notes
Unit II: Intelligent Agents
Focuses on the concept of rational agents and how actions map from sequences to results. Explains the properties of environments and the structure of intelligent agents, including different types such as Simple Reflex Agents, Goal-Based Agents, and Utility-Based Agents.
📘 Unit 2 Notes
Unit III: Search Strategies
Introduces problem-solving using search methods. Discusses Uninformed Search Strategies such as Breadth-First Search, Uniform Cost Search, and Depth-First Search. Also explains Informed Search Strategies like Heuristic Search, Best-First Search, Greedy Search, and the A* Algorithm for optimal solutions.
📘 Unit 3 Notes
Unit IV: Knowledge Representation
Covers the representation of knowledge in AI systems. Topics include Procedural vs. Declarative Knowledge, Logic Programming, Forward and Backward Reasoning, Matching, and Control Knowledge. It also explains reasoning systems for categories, objects, events, mental objects, and default information.
📘 Unit 4 Notes
Unit V: Applications of AI
Explores practical areas where Artificial Intelligence is applied, such as Language Models, Information Retrieval, Information Extraction, Natural Language Processing, Machine Translation, Speech Recognition, and Robotics. Also covers perception and hardware components in AI-driven systems.
📘 Unit 5 Notes
extra ppt
Suggested Readings
- Stuart Russel and Peter Norvig – Artificial Intelligence: A Modern Approach, Pearson Education, 3rd Edition, 2010.
- E. Rich and K. Knight – Artificial Intelligence, TMH, 3rd Edition, 2017.
References
- B. Yagna Narayana – Artificial Neural Networks, PHI, 2005.
- Dan W. Patterson – Artificial Intelligence and Expert Systems, Pearson Education, 2018.
- Joseph C. Giarrantano, Gary D. Riley – Expert Systems: Principles and Programming, Course Technology Inc, 4th Edition, 2004.
- Ivan Bratko – PROLOG Programming for Artificial Intelligence, Pearson, 4th Edition, 2011.
Conclusion
This course offers a complete foundation in Artificial Intelligence, helping students understand both theoretical concepts and real-world applications. By the end of the course, learners will have a clear understanding of intelligent systems, search algorithms, knowledge representation, and how AI is used in modern technologies such as NLP, robotics, and automation.
These notes have been prepared with the help of information collected from Google, textbooks, and other reference materials to make it easier for students to study and understand the topics effectively.