Course Syllabus

Science-stream version organised around the official five-unit syllabus.

Unit 1: Introduction to Artificial Intelligence

  1. Intelligence and Artificial Intelligence
  2. Intelligent Machines and Smart Systems
  3. Definition and Scope of AI
  4. History and Evolution of AI
  5. AI Problem Solving
  6. Search Techniques
  7. Rule-Based Systems
  8. Natural Language Processing
  9. Computer Vision

Unit 2: Machine Learning Fundamentals

  1. Machine Learning and Data-Driven Intelligence
  2. Supervised Learning
  3. Unsupervised Learning
  4. Reinforcement Learning
  5. Training Data and Labelled Data
  6. Regression and Classification
  7. Clustering and Dimensionality Reduction
  8. Neural Networks
  9. Decision Trees
  10. k-Nearest Neighbours (k-NN)
  11. Deep Learning
  12. Model Training and Evaluation
  13. Real-Life ML Applications

Unit 3: AI Applications & Tools — Science Stream

  1. Generic AI Applications
  2. AI in Climate Modelling
  3. Healthcare Diagnostics
  4. Bioinformatics
  5. Environmental Monitoring
  6. Scientific Data Analysis
  7. Research Automation
  8. Hands-on AI Tools

Unit 4: Ethical, Social, Economic and Legal Implications of AI

  1. Ethical & Responsible AI
  2. Algorithmic Bias and Fairness
  3. Privacy and Surveillance
  4. Misinformation and Deepfakes
  5. Intellectual Property & AI Content
  6. AI and Employment
  7. Human–AI Collaboration
  8. Digital Divide
  9. Security Concerns
  10. Laws, Policies and AI Governance
  11. Sustainable AI Development

Unit 5: Group Mini Project & Presentation

  1. Project Goal
  2. Problem Identification
  3. AI Application Analysis
  4. Ethical Considerations
  5. Report Preparation
  6. Seminar Presentation