Fundamentals of Artificial Intelligence
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Fundamentals of Artificial Intelligence Syllabus — Unit-wise Content
The official GTU curriculum for Fundamentals of Artificial Intelligence (subject code 4350705) as prescribed for Nano · Semester 5 divides the course into 5 units, covering 31 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 (4350705)Unit 1 Introductio n to artificial intelligence
- 1.1. The AI Problem
- 1.2. The Underlying Assumption
- 1.3. AI Techniques
- 1.4. The level of model
- 1.5. Criteria for success
- 1.6. Application of AI
Unit 2 State Space Search and Heuristic Technique
- 2.1. Solving problems as state space search
- 2.2. Production system
- 2.3. Problem characteristics
- 2.4. Depth First Search
- 2.5. Breadth-First Search
- 2.6. Heuristic function
- 2.7. Hill climbing
- 2.8. Best First Search
Unit 3 Knowledge Representati o n
- 3.1. Knowledge Representation
- 3.2. Issues in Knowledge Representation
- 3.3. FIRST ORDER LOGIC
- 3.4. Computable function and predicates
- 3.5. Forward/Backward reasoning
- 3.6. Unification and Lifting
- 3.7. Resolution procedure
- 3.8. Logic programming Fundamentals of Artificial Intelligence Course Code: 4350705
Unit 4 Expert System
- 4.1. Expert System
- 4.2. Building Blocks of Expert System
- 4.3. Development phases of Expert System
- 4.4. Expert System-shell
- 4.6. Knowledge Acquisition
- 4.7. Application of the expert system
Unit 5 AI Cas e Studies
- 5.3. Recommendation Algorithm
- 5.4. Digital (Voice) Assistant
- 5.5. Virtual Face Filters Note: The UOs need to be formulated at the ‘Application Level’ and above of Revised Bloom’s Taxonomy’ to accelerate the attainment of the COs and the competency.
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