Machine Learning and Neural Network Design for Biomedical Applications

3.0 credits · Graduate

This one semester course will be focused on exposing students to basic strategies in machine learning using neural networks within the context of biomedical engineering. This includes early uses (classical fitting), and basic to concepts such as loss functions, models, backpropagation and training, as well as current layer (dense, convolutional networks and x-formers), and model architectures (e.g. autoencoders, U-Nets, adversarial networks, and large language models) and how these are applied towards current biomedical engineering tasks (medical image recognition, bioinformatics, etc.). This will be geared towards students who are interested in learning to design, code and understand common neural network strategies. Course materials will be primarily implemented in Python, using common packages, such as NumPy, SciPy, Pandas, and TensorFlow, in addition to open-source databases. Students are expected to have a basic familiarity with python programming and some experience with applying statistical methods.