Semi-Supervised & Transfer Learning for Transportation & Logistics Industries 

This course is designed to provide an in-depth understanding of semi-supervised and transfer learning techniques and their applications in the Transportation & Logistics industries. Participants will learn how to leverage unlabeled data and pre-trained models to improve the performance and efficiency of machine learning solutions for various tasks relevant to transportation planning, logistics optimization, and supply chain management. 

• Understand the principles and motivations behind semi-supervised and transfer learning

• Apply semi-supervised learning algorithms, such as self-training, co-training, and graph-based methods, to leverage unlabeled transportation and logistics data

• Implement and fine-tune pre-trained models for transfer learning in the transportation and logistics domains

• Develop domain adaptation techniques to bridge the gap between source and target domains in transportation and logistics applications

• Deploy semi-supervised and transfer learning solutions for transportation demand forecasting, logistics network optimization, and supply chain risk assessment

1. Introduction to Semi-Supervised Learning

• Overview of semi-supervised learning and its applications in the Transportation & Logistics industries

• Assumptions and scenarios for semi-supervised learning

• Self-training, co-training, and multi-view learning algorithms

• Graph-based semi-supervised learning methods (e.g., label propagation, manifold regularization)

• Hands-on exercises: Implementing semi-supervised learning algorithms on transportation and logistics datasets

2. Transfer Learning and Pre-trained Models

• Introduction to transfer learning and its benefits for the transportation and logistics domains

• Types of transfer learning: feature extraction, fine-tuning, and domain adaptation

• Pre-trained models for time series forecasting (e.g., LSTM, Transformer) and spatial data analysis (e.g., CNN, GNN)

• Fine-tuning strategies and best practices for transfer learning in transportation and logistics applications

• Hands-on exercises: Fine-tuning pre-trained models for transportation demand forecasting and logistics network optimization

3. Domain Adaptation Techniques

• Problem formulation and challenges in domain adaptation for transportation and logistics data

• Discrepancy-based methods (e.g., Maximum Mean Discrepancy, Correlation Alignment)

• Adversarial-based methods (e.g., Domain Adversarial Neural Networks, Adversarial Discriminative Domain Adaptation)

• Reconstruction-based methods (e.g., Domain Separation Networks, Cycle-Consistent Adversarial Networks)

• Hands-on exercises: Implementing domain adaptation techniques for supply chain risk assessment and cross-modal transportation data analysis

4. Applications and Case Studies

• Transportation demand forecasting using semi-supervised learning and transfer learning

• Logistics network optimization with pre-trained models and fine-tuning

• Supply chain risk assessment and resilience analysis using domain adaptation techniques

• Case studies of semi-supervised and transfer learning in the Transportation & Logistics industries

• Hands-on exercises: Developing a semi-supervised or transfer learning solution for a specific transportation or logistics use case

• 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