Advanced Machine Learning. 2 Credit Hours

Catalog description

This advanced approach to machine learning in the biomedical sciences will be mostly problem set- and discussion-based. Background information will be delivered in short lectures on datasets and machine learning concepts. Our plan is to discuss advanced regularization techniques, convolutional and recurrent neural networks, generative adversarial networks, reinforcement learning and attention, transformer and NLP models, explainable AI, and ethical considerations. Each problem set will cover a different area of biomedical research. One problem set and one 2-hour discussion (30 minutes lecture, 30 minutes concept discussion, 1hr problem set progress discussion) every week. The final problem set will be a kaggle competition where the students implement methods of their own choosing and compete to achieve the best model performance. Prerequisites: INTD 6075 Open for Cross Enrollment on Space Available Basis.