DI04000311 · Semester 4 · 5G scheme
Fundamentals of Artificial Intelligence
Notes and solutions following the Gujarat Technological University (GTU) syllabus for DI04000311. Independent material — not issued by the university.
Computer EngineeringComputer Science & Engineering
Materials
PYQ Solutions
Past university papers, fully solved.
Solved past papers — detailed answers
27 pages · English + ગુજરાતી
Premium Preview · 4 pages
Syllabus units
Unit 1: Introduction to Artificial Intelligence
- History and Evolution of AI
- Applications of AI
- Areas of AI: Machine Learning, NLP, Computer Vision, Robotics, Expert Systems
- Advantages and Disadvantages of AI
- Generative AI: Definition and concept
- Agentive AI: Definition and concept
Unit 2: AI Problem-Solving and Search Techniques
- Introduction to AI Characteristics and steps
- Rule-Based Systems: Structure: If-Then rules, Forward Chaining and Backward Chaining, Simple examples using if-else logic
- Problem-Solving: Definition, Problem-Solving Approaches: Uninformed Search: BFS, DFS and Informed (Heuristic) Search: Best-First Search
- AI Agents Overview: Definition and types, Components and examples
- Weak AI vs. Strong AI
Unit 3: Knowledge Representation Techniques
- Introduction and Issues to Knowledge Representation: Need and role of knowledge representation in AI; data vs. information vs. knowledge.
- Logical Representation of Knowledge: Concept of logic, symbols, propositions, and rules.
- First-Order Logic (FOL): Basic syntax and semantics; objects, predicates, and quantifiers with simple examples.
- Reasoning Methods : Concept of inference; Forward Reasoning (Data-driven) and Backward Reasoning (Goal-driven)
- Unification and Lifting: Concept of unification in matching logic expressions
- Resolution Procedure: Simple explanation of how resolution helps in deriving conclusions.
Unit 4: Expert Systems and Knowledge-Based Systems
- Introduction to Expert Systems: Definition, need, and role of Expert Systems; how they differ from traditional programs.
- Characteristics of Expert Systems
- Building Blocks of Expert Systems: Knowledge Base, Inference Engine, User Interface, Explanation Facility, and Knowledge Acquisition Module.
- Development Phases of an Expert System: Problem identification, knowledge acquisition, system design, implementation, testing, and maintenance.
- Knowledge Acquisition: Methods to gather knowledge from experts, data, or rules; challenges in acquiring expert knowledge.
- Applications of Expert Systems: Examples from: medical diagnosis (MYCIN), agriculture, finance, education, customer support, and manufacturing.
Unit 5: AI Tools and Their Applications
- Introduction to AI Tools: Definition, classification, and importance
- Chat GPT: Overview, applications (Content writing and programming), limitations, ethical considerations
- Google AI Tools: Google Translate, Google Lens (overview and applications (Notebook LM, Google Assistant, Gemini)
- Canva AI: Overview and applications (AI Image Generator, Magic Design)
- Netflix Recommendation System: Collaborative filtering, content-based filtering, hybrid approaches
- Alexa Voice Assistant: Speech recognition, NLU, response generation
About this course
Study materials, question papers, and solutions for Fundamentals of Artificial Intelligence (DI04000311) - Computer Engineering / Computer Science and Engineering, Semester 4