Module 1: Introduction to Generative AI 

• Overview of generative AI and its applications
• Introduction to generative models
• Key concepts: generative models vs. discriminative models, probability distributions

Module 2: Fundamentals of Deep Learning 

• Introduction to deep learning and neural networks
• Training neural networks: backpropagation, optimization algorithms
• Regularization techniques: dropout, L1/L2 regularization
• Convolutional Neural Networks (CNNs) for generative tasks

Module 3: Variational Autoencoders (VAEs) 

• Introduction to autoencoders
• Understanding VAEs: encoder, decoder, and latent space
• Variational inference and the reparameterization trick
• Applications of VAEs: image generation, data compression

Module 4: Generative Adversarial Networks (GANs) 

• Introduction to GANs and their components (generator, discriminator)
• GAN training process: minimax game, adversarial loss
• Architectural variations: DCGAN, WGAN, CGAN, etc.
• GAN applications: image synthesis, style transfer

Module 5: Sequence Generation with Recurrent Neural Networks (RNNs) (6 hours) 

• Introduction to RNNs and their variants (LSTM, GRU)
• Applications of RNNs for sequence generation: text generation, music generation
• Training techniques for sequence generation models
• Attention mechanisms for improving sequence generation

Module 6: Reinforcement Learning for Generative Tasks 

• Introduction to reinforcement learning (RL)
• RL basics: Markov Decision Process (MDP), policy gradients
• RL for generative tasks: policy-based methods, generative adversarial imitation learning
• Applications of RL for generative AI: game playing, robotics

Module 7: Advanced Topics and Applications 

• Deep generative models: PixelCNN, Glow, RealNVP
• Adversarial examples and defenses
• Domain adaptation and transfer learning in generative AI
• Ethical considerations and challenges in generative AI

Module 8: Hands-on Projects and Case Studies 

• Practical implementation of generative AI models using popular frameworks (e.g., TensorFlow, PyTorch)
• Guided projects and assignments to reinforce concepts learned
• Case studies showcasing real-world applications of generative AI

Module 9: Future Trends and Conclusion 

• Emerging trends in generative AI research
• Challenges and opportunities in the field
• Final thoughts and wrap-up of the course