- Level -Advanced
- Prerequisites -Year 2 AI coursework or equivalent experience
- Specializing in advanced AI techniques and real-world applications for industry and research.
- Assessments -Weekly practical assignments, Mid-term research paper, Industry project
Module 1 - Advanced Deep Learning and Generative AI (Weeks 1-2)
Learning OutcomeMaster deep learning architectures and generative AI models.
1.1 Advanced CNNs, RNNs, Transformers, and GANs
1.2 AI-powered text, image, and video synthesis (Deepfakes, AI Art, etc.)
1.3 AI model fine-tuning and optimization techniques
Practical Component
Training a GAN model for synthetic media generation
Module 2 - AI for Autonomous Systems and Robotics (Weeks 3-4)
Learning OutcomeExplore AIs role in self-driving cars and intelligent automation.
2.1 AI in autonomous vehicles - LiDAR, sensor fusion, and AI-driven control
2.2 AI-powered drones and UAV systems
2.3 AI for industrial robotics and intelligent automation
Practical Component
Simulating an AI-controlled autonomous system
Module 3 - AI in Edge Computing and IoT (Weeks 5-6)
Learning OutcomeDeploy AI on low-power and real-time systems.
3.1 AI-driven IoT applications for smart homes and industries
3.2 AI at the edge - TensorFlow Lite, TinyML, and on-device AI
3.3 AI-powered embedded systems and wearable devices
Practical Component
Developing an AI-based IoT automation system
Module 4 - AI for Healthcare and Drug Discovery (Weeks 7-8)
Learning OutcomeApply AI to revolutionize medicine and bioinformatics.
4.1 AI in genome analysis and precision medicine
4.2 AI-powered biomedical image processing
4.3 AI in drug discovery and clinical trials
Practical Component
Building an AI model for disease prediction
Module 5 - Ethical AI, Governance, and Regulation (Weeks 9-10)
Learning OutcomeUnderstand AI ethics, policies, and global AI regulations.
5.1 AI transparency, fairness, and accountability
5.2 AI governance frameworks and compliance standards (GDPR, AI Act)
5.3 AIs societal impact - Bias mitigation and responsible AI
Practical Component
Conducting an AI ethics case study
Module 6 - Industry Research & AI Innovation Project (Weeks 11-12)
Learning OutcomeSolve real-world AI challenges through research and industry collaboration
6.1 Identifying an industry challenge for AI-driven innovation
6.2 Identifying an industry challenge for AI-driven innovation
6.3 Publishing findings and presenting to an industry panel
Practical Component
Creating an AI research paper and project prototype