Semester 1: Week 1 - 14
Week 1: Fundamentals of AI
• Introduction to AI
• Understanding the basics of Artificial Intelligence and historical context.
Week 2-3: Fundamentals of Machine Learning
• Review of machine learning concepts.
• Supervised vs Unsupervised learning.
• Basics of neural networks.
Week 4-5: Python and Mathematical Foundation
• Basics of Python
• Introduction to libraries like NumPy, Pandas, and Matplotlib
• Introduction to Linear Algebra, Calculus, and Statistics for ML
• Key concepts that underpin ML algorithms.
Week 6-8: Deep Learning Basics and Reinforcement Learning
• Introduction to deep learning.
• Neural network architectures.
• Activation functions and backpropagation.
• Introduction to reinforcement learning.
• Applying reinforcement learning to generative tasks.
Week 9-10: Introduction to Generative AI
• Introduction to Generative AI
• Understanding generative models.
• Overview of different generative models.
• Probabilistic models vs non-probabilistic models.
• Understanding of Variational Autoencoders (VAEs).
Week 11-12: Natural Language Processing (NLP)
• Introduction to NLP and its challenges.
• Language models and text generation.
• Case studies in NLP using generative models.
Week 13-14: Project: Generative AI Application (Submission)
• Apply Generative AI concepts.
• Students create a simple generative AI project.
Semester 2: Week 15 - 26
Note: Project work commences concurrently with classes starting from the 6th week.
Week 15-16: Recap and Introduction to Cloud Platforms and ML
• Recap of Introduction to Generative AI
• Overview of AWS services like S3, EC2, and SageMaker.for ML Beginners
• Basic Data Processing with AWS
Week 17-18: Generative Adversarial Networks (GANs)
• In-depth study of GANs.
• Training GANs and common challenges.
• Applications of GANs.
Week 19-20: Advanced GANs
• Deep dive into advanced GAN architectures.
• Conditional GANs and InfoGAN.
• StyleGAN and StyleGAN2.
Week 21-22: Transformers and Attention Mechanism
• Understanding transformer architecture.
• Attention mechanisms and their role.
• Applications in generative tasks.
Week 23-24: Transfer Learning in Generative AI
• Transfer learning techniques in generative tasks.
• Fine-tuning pre-trained models.
• Real-world applications and challenges.
Week 25-26: Ethical Considerations in Generative AI
• Discussing ethical concerns in AI and generative models.
• Bias and fairness in AI.
• Responsible AI practices.
Week 27-30: Final Project submission and Review
