Fundamentals of Artificial Intelligence
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Follow on Instagram for UpdatesFundamentals of Artificial Intelligence Syllabus — Unit-wise Content
The official GTU curriculum for Fundamentals of Artificial Intelligence (subject code DI04000311) as prescribed for Chem. Green · Semester 4 divides the course into 5 units, covering 34 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (DI04000311)Unit 1 Introduction to Artificial Intelligence
- History and Evolution of AI Applications of AI Areas of AI: Machine Learning, NLP, Computer Vision
- 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 Problem-Solving: Definition
- Characteristics and steps Rule-Based Systems: Structure: If–Then rules
- Forward Chaining and Backward Chaining
- Simple examples using if–else logic 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
- propositions, and rules. First-Order Logic (FOL): Basic syntax and semantics
- 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 Program Name: Diploma Engineering Level: Diploma Branch: Computer Engineering / Computer Science and Engineering Subject Code : DI04000311 Subject Name : Fundamentals of Artificial Intelligence 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
- testing, and maintenance. Knowledge Acquisition: Methods to gather knowledge from experts
- or rules
- challenges in acquiring expert knowledge. Applications of Expert Systems: Examples from: medical diagnosis (MYCIN)
- 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)
- 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
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