Generative AI Techniques
Course Overview:
This course delves into the exciting world of Generative AI, a branch of Artificial Intelligence (AI) focused on creating new and original data. You'll explore how Generative AI techniques can revolutionize Supply Chain Management (SCM) by enabling data augmentation, forecasting, and creative problem-solving.
Learning Objectives:
Define Generative AI and its core functionalities (data generation, adversarial learning).
Understand different Generative AI techniques: Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs).
Explore how Generative AI can be applied to augment and enrich datasets for improved SCM analysis.
Identify the potential of Generative AI for demand forecasting and scenario planning in supply chains.
Analyze the ethical considerations surrounding the use of Generative AI in data creation.
Course Highlights:
1. Introduction to Generative AI
Introduction to Generative AI: The power to create new data.
Understanding Generative Adversarial Networks (GANs): The core concepts of generators and discriminators.
Exploring the training process of GANs and their applications in data augmentation.
Hands-on Exercises: Utilizing online tools to experiment with basic GAN functionalities.
Case Studies: How Generative AI is used to augment datasets for forecasting and anomaly detection in SCM.
2. Generative AI Applications in SCM
Introduction to Variational Autoencoders (VAEs): An alternative approach to data generation.
Understanding how VAEs learn latent representations and generate new data points.
Exploring the potential of Generative AI for demand forecasting: Creating synthetic demand patterns.
Scenario planning with Generative AI: Simulating potential disruptions and testing supply chain resilience.
Discussion on the ethical considerations surrounding bias and control in Generative AI data creation.
Prerequisites:
Strong understanding of deep learning concepts and architectures (e.g., CNN, RNN, Transformers)
Proficiency in programming with Python and deep learning frameworks (e.g., TensorFlow, PyTorch)
Familiarity with probability theory and statistical concepts