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 Finance & Insurance industries. Participants will learn how to represent complex data structures, such as time series, graphs, and text, in a lower-dimensional space while preserving their semantic relationships. The course covers various embedding techniques and their applications in financial modeling, risk assessment, and fraud detection.

• Understand the concept of embeddings and their applications in the Finance & Insurance industries

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

• Apply embeddings to solve financial modeling tasks, such as stock price prediction and portfolio optimization

• Utilize graph embeddings for risk assessment and fraud detection in insurance networks

• Leverage text embeddings for sentiment analysis and document classification in financial news and reports

1. Introduction to Embeddings

• Overview of embeddings and their role in unsupervised learning

• Applications of embeddings in the Finance & Insurance industries

• Vector space models and their properties

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

2. Time Series Embeddings

• Time series data in the Finance & Insurance industries (e.g., stock prices, economic indicators)

• 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 Finance & Insurance industries (e.g., stock price prediction, portfolio optimization)

• Hands-on exercises: Developing a time series embedding model for financial time series data

3. Graph Embeddings

• Introduction to graph theory and network analysis in finance and insurance

• Random walk-based embeddings (DeepWalk, node2vec) for insurance fraud detection

• Matrix factorization-based embeddings (Laplacian Eigenmaps, Graph Factorization) for risk assessment in financial networks

• Applications of graph embeddings in the Finance & Insurance industries (e.g., credit risk modeling, customer relationship management)

• Hands-on exercises: Implementing graph embedding techniques on financial and insurance network data

4. Text Embeddings and Applications

• Text data in the Finance & Insurance industries (e.g., financial news, company reports)

• Word embeddings (Word2Vec, GloVe, FastText) for financial sentiment analysis

• Document embeddings (Doc2Vec, TF-IDF) for document classification and clustering

• Contextualized embeddings (ELMo, BERT) for named entity recognition and relation extraction in financial texts

• Hands-on exercises: Applying text embedding techniques to solve a real-world Finance or Insurance problem

• 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