- Level - Intermediate - Choice - Specialisation
- Understanding AI agents, their classifications, and their impact across industries, with a focus on Interaction AI.
- Prerequisites -Introduction to AI or any foundational AI course
- Assessments - Micro Assessment weekly, Full Assessment last week.
Week 1 - Understanding AI Agents
Learning OutcomeStudents will understand the fundamental types of AI agents and their functions.
1.1 Defining AI Agents: Reactive, Deliberative, and Hybrid.
1.2 Autonomy, Adaptability, and Goal-Oriented Behavior.
1.3 AI Agents in Automation, Robotics, and Digital Assistants.
Practical Component
Hands-on experience with different types of AI agents, analyzing their behavior in simulations and real-world applications.
Week 2 - Interaction and Decision-Making
Learning OutcomeUnderstanding how AI agents make decisions and interact in various environments.
2.1 AI Agent Decision Trees and Reinforcement Learning.
2.2 Multi-Agent Systems: Collaboration vs. Competition.
2.3 Ethical Considerations in AI Agent Autonomy.
Practical Component
Simulating AI agent decision-making processes and analyzing case studies.
Week 3 -Personalization and Adaptability
Learning OutcomeExploring how AI agents learn and personalize experiences.
3.1 Context Awareness in AI Agents.
3.2 Personalization in Virtual Assistants and Chatbots.
3.3 Adapting to User Behavior Over Time.
Practical Component
Implementing AI-driven personalized experiences in Interaction AI.
Week 4 - AI Agents in Real-World Applications
Learning OutcomeExploring AI agents in different domains, including business and research.
4.1 AI Agents in Finance, Healthcare, and Customer Service.
4.2 AI in Gaming, Simulations, and Training Environments.
4.3 Human-AI Collaboration and Decision Support Systems.
Practical Component
Hands-on development of AI-driven solutions in a selected domain.
Week 5 - Building AI Agents from Scratch
Learning OutcomeCreating AI agents with different functionalities for real-world applications.
5.1 Implementing Decision Models in AI Agents.
5.2 Integrating Perception, Memory, and Learning.
5.3 Evaluating Performance Metrics for AI Agents.
Practical Component
Developing and testing a custom AI agent.
Week 6 - Project and Assessment
Learning OutcomeStudents will develop a real-world AI agent-based project.
6.1 Defining Objectives and Choosing an AI Agent Architecture.
6.2 Model Training, Self and Peer Assessment.
6.3 Final Presentations and Demonstrations.
Practical Component
Live presentations showcasing the AI agents built by students.