Subject Overview

An advanced study of artificial intelligence and expert systems, integrating classical symbolic AI, knowledge representation, automated reasoning, machine learning, intelligent decision support, modern generative AI, enterprise implementation, and responsible governance.

Learning Goal

Enable students to design, evaluate, deploy, and govern AI and expert-system solutions; critically select appropriate models and reasoning methods; support strategic and managerial decisions; and assess contemporary research, ethical risks, and future developments.

Syllabus

1. Foundations and Evolution of Artificial Intelligence

Learning Objective: Explain AI terminology, history, philosophical foundations, intelligent agents, rationality, problem types, AI paradigms, the Turing Test, and the relationship among AI, machine learning, data science, and expert systems.

Category: foundation

2. Intelligent Agents and Problem Formulation

Learning Objective: Model environments and problems using agent architectures, PEAS specifications, state spaces, goals, utilities, constraints, performance measures, and rational decision criteria.

Category: foundation

3. Search, Optimization, and Constraint Satisfaction

Learning Objective: Apply uninformed and informed search, heuristic design, local and adversarial search, minimax, alpha–beta pruning, constraint-satisfaction methods, and evolutionary or metaheuristic optimization.

Category: core

4. Knowledge Representation and Ontologies

Learning Objective: Represent domain knowledge using propositional and first-order logic, semantic networks, frames, scripts, production rules, knowledge graphs, description logics, taxonomies, and ontologies.

Category: core

5. Automated Reasoning and Inference

Learning Objective: Use deduction, induction, abduction, resolution, forward and backward chaining, theorem proving, non-monotonic reasoning, truth maintenance, and reasoning under incomplete knowledge.

Category: core

6. Uncertainty, Probabilistic Reasoning, and Decision Theory

Learning Objective: Analyze uncertain decisions using probability, Bayesian networks, Markov models, fuzzy logic, certainty factors, Dempster–Shafer theory, utility theory, and influence diagrams.

Category: core

7. Expert-System Architecture and Knowledge Engineering

Learning Objective: Design expert systems comprising knowledge bases, inference engines, working memory, explanation facilities, and user interfaces; conduct knowledge acquisition, elicitation, verification, validation, maintenance, and shell-based development.

Category: core

8. Machine Learning for Intelligent Systems

Learning Objective: Evaluate supervised, unsupervised, semi-supervised, and reinforcement learning; feature engineering, model selection, ensembles, evaluation metrics, bias–variance trade-offs, and integration of learned models with symbolic systems.

Category: core

9. Neural Networks, Deep Learning, and Generative AI

Learning Objective: Examine neural architectures, representation learning, convolutional and recurrent networks, transformers, foundation models, large language models, prompting, retrieval-augmented generation, fine-tuning, and hallucination control.

Category: contemporary

10. Natural Language, Perception, and Multimodal Intelligence

Learning Objective: Apply AI to language understanding, speech, computer vision, information extraction, conversational systems, multimodal reasoning, and human–AI interaction while assessing relevant quality measures.

Category: application

11. Planning, Reinforcement Learning, Robotics, and Multi-Agent Systems

Learning Objective: Formulate sequential intelligence using classical and probabilistic planning, Markov decision processes, reinforcement learning, robotic perception and control, coordination, negotiation, and distributed multi-agent architectures.

Category: advanced

12. AI Analytics and Intelligent Decision Support

Learning Objective: Develop prescriptive analytics and hybrid expert systems for diagnosis, forecasting, recommendation, risk assessment, optimization, scenario analysis, and managerial decision support under uncertainty.

Category: application

13. Enterprise AI Strategy and Digital Transformation

Learning Objective: Align AI initiatives with organizational strategy; evaluate use cases, data readiness, build-versus-buy choices, economics, return on investment, process redesign, workforce impacts, leadership, change management, and industry adoption.

Category: strategic

14. AI Engineering, Deployment, and Lifecycle Management

Learning Objective: Engineer production AI using data pipelines, APIs, cloud and edge platforms, MLOps, LLMOps, testing, monitoring, drift detection, cybersecurity, scalability, reliability, model documentation, and continuous improvement.

Category: application

15. Explainability, Ethics, Governance, and Future Directions

Learning Objective: Critically assess explainable AI, fairness, privacy, safety, accountability, intellectual property, human oversight, auditing, standards, regulation, sustainability, and emerging developments including neuro-symbolic, causal, autonomous, and trustworthy AI.

Category: contemporary