Course Description and Goals

The fields of computational biology and biomedical research are evolving rapidly, fueled by the transformative power of deep learning. This course, “Deep Learning Methods in Biomedical Sciences with PyTorch,” offers an in-depth exploration of how advanced computational techniques intersect with complex biomedical data to drive new discoveries.

As biomedical datasets expand in size and complexity, traditional algorithms often struggle to capture subtle patterns and relationships. In contrast, deep learning—through multi-layered neural networks—offers robust solutions for some of the field’s most challenging problems.

This course is designed to equip students with both theoretical knowledge and practical skills for pioneering biomedical research. Using PyTorch, a leading deep learning framework, students will gain hands-on experience in implementing, training, and evaluating neural network models on real-world biomedical data.

The curriculum is thoughtfully structured to build foundational understanding before moving to advanced topics, including:

• Core neural network architectures
• Convolutional neural networks for medical image analysis
• Recurrent neural networks for sequential health data
• Transformers for genomic sequences
• Specialized models like BioBERT for clinical text

Beyond algorithms and coding, the course emphasizes real-world impact. Through case studies, projects, and interactive discussions, students will engage with pressing biomedical challenges, such as:

• Disease prediction
• Medical image segmentation
• Genomic data interpretation

By the end of the course, students will not only master deep learning methods but also appreciate their potential to shape the future of biomedical science. This is more than a class—it’s an invitation to the forefront of biomedical innovation, leveraging the power of deep learning to decipher the complexities of life and health.