Subject: Data Science
Course Number: 224
Course Title: Understanding Machine Learning: From Theory to Applications
Units: 3
School: Graduate Division
Department: Health Data Science Program

Course Description: This course teaches the mathematical foundations of machine learning (ML). Each week, the course surveys a different algorithm to examine its underlying machinery, covering topics such as linear algebra, calculus, and optimization. ML algorithms range from linear models to gradient boosting and deep learning. The course also discusses newer concepts such as model fairness and ML for causal inference. Upon course completion, students should be able to learn new ML algorithms independently.
Prerequisites: BIOSTAT 216
Restrictions: This course is part of the Health Data Science Masters and Certificate Program and may have space limitations. Auditing is not permitted.
Activities: Direct - Lecture, Direct - Project, Direct - Discussion, Student - Lecture

Instructor of Record: J. Feng
May the student choose the instructor for this course? No
Does enrollment in this course require instructor approval? No

Quarter(s) Offered: Spring
Course Grading Convention: Letter Grade, P/NP (Pass/Not Pass) or S/U (Satisfactory/Unsatisfactory)
Graduate Division course: Yes
Is this a web-based online course? No
Is this an Interprofessional Education (IPE) course? No
May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes
Repeat course for credit? No