Curriculum / DescriptionsBME 312: Biomedical Applications in Machine Learning
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Prerequisites
BME 220 (for statistical analysis and Python applications), EA1 (for linear algebra background). Math 220-1 and 220-2 (for single-variable calculus background). Coding knowledge is encouraged (preferably in Python). For any equivalent classes, please get in touch with the instructorDescription
The course will start with a brief overview of how to upload and handle various types of biomedical data using Python. Supervised learning tasks such as regression and classification will be implemented in Python using data from heart disease data, electromyography and Parkinson's disease. Various Machine Learning models such as Linear Regression, Logistic Regression, Random Forests and Deep Learning will be introduced to fit and classify biomedical data. Unsupervised learning approaches to unlabeled data, such as clustering and dimensionality reduction will be applied to both structured (numerical) and image data. Finally, some new frontiers in machine learning will be presented and how they can be applied to biomedical problems. All models will be implemented in Python, either from scratch or using high-level libraries.
Who Takes It?
Junior/Senior undergraduates, MS and PhD students
Mini-syllabus
- Linear Regression
- Classification using Logistic Regression
- Model Evaluation
- K Nearest Neighbors
- Tree-Based Models
- Deep Neural Networks
- Convolutional Neural Networks
- Clustering
- Dimensionality Reduction
Textbook/Required Materials
None
