Generative AI Professional Training Program

• Module 1: Foundations of Generative AI (10 credit hours)

• Introduction to Generative Modeling: Key concepts, history, and probabilistic foundations.
• Autoregressive Models: RNNs, LSTMs, Transformers, and sequence generation.
• Variational Autoencoders (VAEs): Latent space representations, encoding and decoding, applications.
• Generative Adversarial Networks (GANs): Adversarial training, different GAN architectures, and training techniques.

• Module 2: Advanced Generative Models (15 credit hours)

• Diffusion Models: Theory, sampling techniques, applications in image and audio generation.
• Transformer-based Language Models: BERT, GPT, PaLM, and their applications in text generation, translation, and dialogue.
• Multimodal Generative Models: Generating content across different modalities (text, image, audio, video).

• Module 3: Applications of Generative AI (20 credit hours)

• Natural Language Processing: Text generation, dialogue systems, machine translation, summarization, question answering.
• Computer Vision: Image generation, image editing, style transfer, 3D model generation.
• Audio and Music Generation: Speech synthesis, music composition, sound design.
• Drug Discovery and Design: Molecule generation, protein design, and property prediction.
• Content Creation: Art, writing, music, and video generation.

• Module 4: Responsible AI and Deployment (15 credit hours)

• Ethical Considerations: Bias, fairness, transparency, accountability, and societal impact.
• Security and Privacy: Protecting sensitive data, preventing malicious use, and ensuring robustness.
• Model Explainability and Interpretability: Understanding and interpreting generative model outputs.
• Deployment and Monitoring: Deploying generative AI systems, monitoring performance, and mitigating risks.
• Case Studies and Best Practices: Analyzing real-world examples of responsible generative AI development and deployment.

• Assessment:

• Assignments: Programming assignments involving the implementation and evaluation of generative models.
• Projects: Individual or group projects focusing on specific applications of generative AI.
• Presentations: Presenting research papers or project findings to the class.
• Final Exam: Comprehensive exam covering the theoretical and practical aspects of the course.
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• Generative AI for Code: Code generation, code completion, and code summarization.

80 hr

• Upon successful completion of this course, students will be able to:

• Deeply understand the theoretical foundations of various generative AI models.
• Implement and train state-of-the-art generative models using deep learning frameworks.
• Apply generative AI techniques to solve real-world problems in various domains.
• Critically evaluate the performance and limitations of different generative models.
• Develop and deploy generative AI systems responsibly, considering ethical, security, and privacy implications.
• Stay abreast of the latest advancements and research trends in the field of generative AI.