Embedding

This course is designed to provide a deep understanding of embeddings, a powerful unsupervised learning technique, with a focus on its applications in the Transportation & Logistics industries. Participants will learn how to represent complex data structures, such as text, graphs, and time series, in a lower-dimensional space while preserving their semantic relationships. The course covers various embedding techniques and their applications in transportation and logistics, such as demand forecasting, route optimization, and anomaly detection.

• Understand the concept of embeddings and their applications in the Transportation & Logistics industries

• Implement and evaluate various embedding techniques for text, graphs, and time series data

• Apply embeddings to solve demand forecasting, route optimization, and anomaly detection problems

• Utilize graph embeddings for transportation network analysis and optimization

• Leverage time series embeddings for sensor data analysis and predictive maintenance in logistics

1. Introduction to Embeddings

• Overview of embeddings and their role in unsupervised learning

• Applications of embeddings in the Transportation & Logistics industries

• Vector space models and their properties

• Hands-on exercises: Implementing basic embedding techniques (e.g., one-hot encoding, bag-of-words) for transportation data

2. Text Embeddings

• Word embeddings (Word2Vec, GloVe, FastText) for transportation and logistics text data

• Document embeddings (Doc2Vec, TF-IDF) for logistics documents and reports

• Contextualized embeddings (ELMo, BERT) for sentiment analysis and text classification

• Applications of text embeddings in the Transportation & Logistics industries (e.g., customer feedback analysis, document categorization)

• Hands-on exercises: Training and evaluating text embedding models on transportation and logistics text data

3. Graph Embeddings

• Introduction to graph theory and transportation network analysis

• Random walk-based embeddings (DeepWalk, node2vec) for transportation network optimization

• Matrix factorization-based embeddings (Laplacian Eigenmaps, Graph Factorization) for route recommendation

• Applications of graph embeddings in the Transportation & Logistics industries (e.g., supply chain optimization, logistics network design)

• Hands-on exercises: Implementing graph embedding techniques on transportation network data

4. Time Series Embeddings and Applications

• Time series data in the Transportation & Logistics industries (e.g., sensor data, demand data)

• Time series embedding techniques (e.g., Dynamic Time Warping, Time Series Kernels)

• Deep learning approaches for time series embeddings (e.g., Recurrent Autoencoders, Temporal Convolutional Networks)

• Applications of time series embeddings in the Transportation & Logistics industries (e.g., demand forecasting, predictive maintenance)

• Hands-on exercises: Developing a time series embedding model for sensor data analysis in logistics

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

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

• Familiarity with unsupervised learning concepts and dimensionality reduction techniques