Machine Learning

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.

HDS 5101. Principles of Data Science

This course is an introduction to data science. Data science is the study of data to extract knowledge and meaningful insights from noisy data. It is an emerging interdisciplinary field that uses techniques and theories from mathematics, statistics, computer sciences, and domain knowledge to analyze large amounts of data. The objective of this course is to provide students with a principled introduction to data science that properly combines problem solving skills and computational thinking. Students will learn the fundamental pipeline of data science, ranging from data acquisition, data clean-up, data exploration and visualization, modeling and inference, to professional reporting. This course will cover fundamental concepts, methods and tools in data science and how to apply data science methods in health science and biomedical research.

QDS 5301. Introduction to the Analysis of Public Health Data

This course will introduce students to the analysis of various types of data used in public health. The initial section deals with different types of random variables, how their distributions are summarized and displayed graphically. Next, the concepts of hypothesis testing are discussed, along with a variety of measures used in public health. The course concludes with an overview of various methods used to model data, including linear regression and alternative regression approaches.