Semi-Supervised learning & Transfer Learning

This course is designed to provide an in-depth understanding of semi-supervised learning and transfer learning techniques and their applications in the Healthcare & Life Sciences 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 the healthcare and life sciences domains, such as medical image classification, drug repurposing, and disease prediction.

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

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

• Implement and fine-tune pre-trained models for transfer learning in the healthcare and life sciences domains

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

• Deploy semi-supervised learning and transfer learning solutions for medical image classification, drug repurposing, and disease prediction

1. Introduction to Semi-Supervised Learning

• Overview of semi-supervised learning and its applications in the Healthcare & Life Sciences 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 medical datasets

2. Transfer Learning and Pre-trained Models

• Introduction to transfer learning and its benefits for the healthcare and life sciences domains

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

• Pre-trained models for medical image analysis (e.g., ResNet, DenseNet) and natural language processing (e.g., BERT, BioBERT)

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

• Hands-on exercises: Fine-tuning pre-trained models for medical image classification and biomedical text mining

3. Domain Adaptation Techniques

• Problem formulation and challenges in domain adaptation for medical 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 medical image segmentation and disease prediction

4. Applications and Case Studies

• Medical image classification using semi-supervised learning and transfer learning

• Drug repurposing and drug-target interaction prediction with pre-trained models and fine-tuning

• Disease prediction and patient stratification using domain adaptation techniques

• Case studies of semi-supervised learning and transfer learning in the Healthcare & Life Sciences industries

• Hands-on exercises: Developing a semi-supervised learning or transfer learning solution for a specific healthcare or life sciences 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