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 3170319) as prescribed for Biomed · Semester 7 divides the course into 5 units, covering 8 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 (3170319)Unit 1 Introduction to AI
- Introduction - The Turing Test - Roots of AI - Techniques Used in AI - Subfields of AI - Considerations for Knowledge Representation
Unit 2 Introduction to ML
- Introduction - Types of Machine Learning - Discipline of Machine Learning - Learning Model - Clases of Learning - Inductive Learning - Discovery based Learning - Reinforcement Learning -Symbol Based Learning- Explanation Based Learning -Machine Learning Application- Basic Research Problems in Machine Learning
Unit 3 Expert Systems
- Rule-based expert systems - knowledge representation - expert system development - Structure - characteristics - Forward chaining and backward chaining inference - Advantages and disadvantages - Uncertainty management in rule-based expert systems - probability theory - Bayesian reasoning - FORECAST: Bayesian accumulation of evidence
Unit 4 Implementation of AI algorithms
- Classification and Regression Using Supervised Learning - Detecting Patterns with Unsupervised Learning - Predictive Analytics with Ensemble Learning - Logic Programming - Case Study: Building A Speech Recognizer
- Object Detection and Tracking
Unit 5 Fuzzy Systems
- Classical set Vs Fuzzy set – Operation and Properties –Fuzzy Relations – Fuzzy Logic control – Fuzzification
- Membership functions- Defuzzification
- Rule Based System, and Applications
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