Introduction to Artificial Intelligence
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Follow on Instagram for UpdatesIntroduction to Artificial Intelligence Syllabus — Unit-wise Content
The official GTU curriculum for Introduction to Artificial Intelligence (subject code 2720510) as prescribed for EAC( · Semester 2 divides the course into 9 units, covering 61 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 (2720510)Unit 1 Introduction
- What is artificial Intelligence? Major areas of Artificial Intelligence
- Introduction to AI Problems and applications
- Defining problems as a state space search
- Production systems. 3 7.5
Unit 2 Search techniques
- Breadth first search
- Depth first search
- Hill climbing
- Best first search
- AO* Algorithm
- Iterative Deepening Search
- Recursive Best First Search
- Constraint Satisfaction and Heuristic Repair
Unit 3 Game Playing
- Introduction to Game playing
- The Minimax Search Procedure
- Alpha- Beta Procedure
- The Search Efficiency of Alpha-Beta Procedure
- Recent applications
Unit 4 Knowledge Representation
- Production rules
- Predicate Calculus- Rules of Inference
- Semantics and Deduction
- Soundness and completeness of rules
- Resolution refutation
- Semantic Nets
- symbolic reasoning
- statistical reasoning
Unit 5 Learning
- Rote learning
- learning by taking advice
- learning in problem solving
- learning from examples
Unit 6 Uncertain Reasoning
- Joint probability
- Marginal probability
- Probabilistic reasoning and 6 15 Bayes Nets
- forward reasoning versus backward reasoning
- Certainty Factors
- Fuzzy set theory
- Fuzzy relation
- Fuzzy value assignment methods
- Inference and Composition methods- Min-Max composition
- max product composition
- Defuzzification methods
- Applications and recent developments
Unit 7 Statistical Methods
- Frequency distributions
- Data analysis
- Expectations and moments
- Co- relation and regression
- Trend analysis
- Seasonal effects
- Cyclical fluctuation
- Moving average, MSE, Predictions 3 7.5
Unit 8 Expert Systems (ES)
- Advantages and characteristics of Expert System
- Knowledge engineering
- Steps in Developing an Expert System
- ES Applications and recent developments
Unit 9 Connectionist Models
- Introduction to Neural Network
- Activation functions
- Supervised and Unsupervised Learning
- Neuro Processing and Neural Network Learning
- Learning rules
- Single layer Perceptrons and Classification
- Introduction to Multilayer Neural Networks
- Neural Network Applications and recent developments
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