Semi-Supervised learning & Transfer Learning

This course is designed to provide an in-depth understanding of semi-supervised and transfer learning techniques and their applications in the Finance & Insurance 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 finance and insurance, such as risk assessment, fraud detection, and customer segmentation.

• 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 financial data

• Implement and fine-tune pre-trained models for transfer learning in the finance and insurance domains

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

• Deploy semi-supervised and transfer learning solutions for risk assessment, fraud detection, and customer segmentation

1. Introduction to Semi-Supervised Learning

• Overview of semi-supervised learning and its applications in the Finance & Insurance 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 financial datasets

2. Transfer Learning and Pre-trained Models

• Introduction to transfer learning and its benefits for the finance and insurance domains

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

• Pre-trained models for financial time series analysis (e.g., LSTM, Transformer) and natural language processing (e.g., BERT, FinBERT)

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

• Hands-on exercises: Fine-tuning pre-trained models for financial time series forecasting and sentiment analysis

3. Domain Adaptation Techniques

• Problem formulation and challenges in domain adaptation for financial 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 credit risk assessment and insurance fraud detection

4. Applications and Case Studies

• Risk assessment and credit scoring using semi-supervised learning and transfer learning

• Fraud detection and anti-money laundering with pre-trained models and fine-tuning

• Customer segmentation and targeted marketing using domain adaptation techniques

• Case studies of semi-supervised and transfer learning in the Finance & Insurance industries

• Hands-on exercises: Developing a semi-supervised or transfer learning solution for a specific finance or insurance 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