Generative AI for data analytics

Overview

Artificial Intelligence (AI) is Increasingly Dominating Technology and Has Major Implications for Society

Syllabus

• Introduction to Generative AI
Overview of AI and its societal impact
Key concepts in generative AI
Generative versus discriminative models
• Foundations of Data Analytics
Basic statistics and probability
Data types and structures
Introduction to data preprocessing and cleaning
• Generative Models in AI
Probabilistic graphical models
Variational autoencoders (VAEs)
Generative adversarial networks (GANs)
Diffusion models
• Applications of Generative AI in Data Analytics
Data augmentation and synthetic data generation
Anomaly detection using generative models
Predictive modeling enhancements
• Tools and Frameworks for Generative AI
Overview of popular libraries (TensorFlow, PyTorch, etc.)
Setting up an environment for generative AI
• Building and Training Generative Models
Data preprocessing for generative models
Model architecture design
Training techniques and optimization
• Evaluating Generative Models
Metrics for assessing model quality
Comparing generative and real data
Model validation and testing
• Advanced Topics in Generative AI
Ethics and biases in generative AI
Interpretable AI in generative models
Recent advancements and research directions
• Case Studies and Practical Projects
Real-world applications of generative AI in analytics
Hands-on projects with datasets
• Conclusion and Future Directions
Recap of key concepts
Future trends in generative AI and data analytics
Opportunities for further learning and research

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Taught by

Mike X Cohen

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Subjects

Data Science