Quantitative Biomedical Research Center

BME 5096-01: Machine Learning

16 weeks starting on Aug 16, 2022

Term: Fall 2022
Days & Times: Tu/Thu, 10:30 am-12:00 pm

Introduction to methods for hypothesis testing and statistical inference, and statistical learning methods for prediction and classification.
In the first five weeks, the course will cover how to analyze different types of data, including analysis methods for continuous, categorical, and survival. Upon completion of the first ten weeks, students should be able to think critically about data and apply appropriate statistical inference procedures to draw conclusions from such analyses.
In the last three weeks, the course will discuss deep learning, Bayesian statistics, causal inference and computational approaches for predictive modeling and data mining.

January 10, 2022 – May 6, 2022

Term: Spring 2022
Days & Times: Wednesday, 5:30-8:30pm

Students will be introduced to a number of computational domains that are gaining an increasing importance in Health Informatics. Specific topics include Deep Learning for Healthcare, Introduction to Python I, Python II, and Introduction to R for Beginners, Level 1 & 2.
Machine Learning I will provide foundational understanding of machine learning models such as logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, artificial neural networks, and machine learning (ML) algorithms and will demonstrate how these models can solve complex problems diagnosis, image recognition, and understanding of text.

August 23 - December 13

Term: Fall Semester, 2018
Days & Times: Thursdays, 11am-12:30pm

This will be a project-focused course. Each student will be required to finish at least 2 projects. Basic data analysis and programming skills are pre-required for this course.
Course projects will cover different types of data and applications.
In the first 4 weeks, the course will cover the concepts of Machine Learning and Artificial Intelligence in Medicine, overview of different computational methods, project descriptions and assignments.
In the following 12 weeks, students will discuss and presents projects guided by instructors.