Deep Learning with Applications to Genomics and Health Informatics

Catalog description

This course covers basic theory and applications for modern deep learning methods in genomics and health informatics. It emphasizes topics of neural networks in deep learning domain and includes coding lab sessions to reinforce the material covered in class. We will explore not only basic theory for deep neural networks but also a range of classic and contemporary learning algorithms and models, including deep feed-forward networks, optimization and computation theories, convolutional neural networks, recurrent neural networks, generative models like transformers (the backbone of ChatGPT), etc. Students will apply deep learning models to biomedical problems and gain hands-on experience using instructor-recommended programming languages and open-source frameworks.