Artificial Intelligence
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Follow on Instagram for UpdatesArtificial Intelligence Syllabus — Unit-wise Content
The official GTU curriculum for Artificial Intelligence (subject code ME01095021) as prescribed for AIAD · Semester 1 divides the course into 6 units, covering 48 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 (ME01095021)Unit 1 Introduction
- Philosophy of artificial intelligence
- Evolution of AI, Applications of AI, Classification of AI, conversational AI vs Generative AI, Intelligent Agents: Agents and Environment
- Nature of Environment
- Structure Environment
Unit 2 Searching based problem solving
- 8 Program Name: Master of Engineering Level: PG Branch: Computer Engineering (Artificial Intelligence And Data Science) Course / Subject Code : ME01095021 Subject Name : Artificial Intelligence Problem Solving Agent
- Blind Search
- Performance measures
- Informed Search
- Introduction to Heuristics
- Variants of heuristic search
- uniform cost
- Overview of Hill Climbing
- Simulated Annealing
- Genetic Algorithms
- Adversarial Search – Minimax
- Alpha beta pruning
Unit 3 Knowledge Representation and Reasoning
- Introduction to Knowledge Representation
- Knowledge-based Agents Logic Based Representation: Prepositional logic (PL)
- First-order logic (FoL)
- Using First-order logic
- Inference in First-order logic
- forward and Backward Chaining Reasoning under uncertainty: Definition of uncertainty
- Utility Based System
- Bayes Rule
- Belief Network
- Markov decision processes knowledge representation and reasoning through fuzzy logic and Bayesian networks
Unit 4 Planning
- The Blocks World
- Components of a Planning System
- Goal Stack Planning
- Nonlinear Planning Using Constraint Posting
- Hierarchical Planning
- Reactive Systems
Unit 5 learning Systems
- Machine learning (supervised
- reinforcement learning - Basics of Reinforcement learning
- Active and Passive reinforcement learning
- Applications Making simple decisions: combining beliefs and desires
- The basis of utility theory
- Utility functions
- multi attribute utility functions
- decision networks – The value of information – Decision theoretic expert systems Sequential Decision problems – value iteration – policy iteration – Partially Observable MDPs Decisions with Multiple Agents: Game Theory – Mechanism Design. soft computing
- Artificial Neural Network
Unit 6 Ethics, challenges and societal impact of AI
- Ethical Initiative: International ethical initiatives-Ethical harms and concerns-Case study: healthcare robots
- Autonomous Vehicles
- Warfare and weaponization
- Program Name: Master of Engineering Level: PG Branch: Computer Engineering (Artificial Intelligence And Data Science) Course / Subject Code : ME01095021 Subject Name : Artificial Intelligence Ethics: Human vs Robot
- robustness and transparency of AI systems
- data bias and fairness of AI system
- privacy and human AI interaction Challenges and opportunities: Challenges - Opportunities- ethical issues in artificial intelligence- Societal Issues Concerning the Application of Artificial Intelligence in Medicine- decision-making role in industries- National and International Strategies on AI
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