Semi-Supervised learning & Transfer Learning

This course explores two powerful AI techniques that can revolutionize your approach to data analysis in Supply Chain Management (SCM): Semi-supervised learning and Transfer learning. You'll discover how to leverage limited labeled data, common in SCM, and harness knowledge from existing models to build robust AI solutions for optimizing your supply chains.

• Define Semi-Supervised Learning and its core principles for leveraging unlabeled data.

• Understand different semi-supervised learning algorithms (e.g., self-training, label propagation) and their applications in SCM.

• Explore the concept of Transfer Learning and its effectiveness in utilizing pre-trained models for new tasks within SCM.

• Identify real-world applications of Semi-Supervised and Transfer Learning in Supply Chain Management (e.g., anomaly detection in sensor data, demand forecasting with limited historical data).

• Analyze the advantages and limitations of these techniques compared to traditional supervised learning.

1. The Power of Limited Data

Introduction to Semi-Supervised Learning: Learning from a combination of labeled and unlabeled data.

Understanding the challenges of limited labeled data in SCM and how semi-supervised learning helps address them.

Exploring different semi-supervised learning algorithms: Self-training and its approach to creating pseudo-labels.

Hands-on Exercises: Utilizing online tools or libraries to experiment with basic semi-supervised learning techniques on SCM data (e.g., sensor data anomaly detection).

Case Studies: Exploring applications of semi-supervised learning for predictive maintenance in SCM, leveraging sensor data to identify potential equipment failures.

2. Transfer Learning - Pre-trained Knowledge for Faster Results

Introduction to Transfer Learning: Leveraging knowledge from existing models for new tasks.

Understanding how pre-trained models work and how they can be adapted to new SCM challenges.

Exploring different transfer learning approaches (fine-tuning, feature extraction) for various SCM applications.

Hands-on Exercises: Utilizing pre-trained models (e.g., for image classification) and fine-tuning them for a chosen SCM task (using online tools or basic coding).

Case Studies: Exploring applications of transfer learning in demand forecasting, leveraging pre-trained models on historical sales data to improve prediction accuracy.

Course Wrap-up: Addressing limitations of both techniques and responsible AI practices in SCM implementations.

• Strong understanding of linear algebra, calculus, and probability theory

• Proficiency in programming with Python and deep learning frameworks (e.g., TensorFlow, PyTorch)

• Familiarity with supervised learning concepts and techniques (e.g., classification, regression, neural networks)

• Knowledge of unsupervised learning methods (e.g., clustering, dimensionality reduction) is beneficial but not required