INFO 534: Applied Machine Learning, Fall Course Offering
The elective course gives an introduction to machine learning techniques and theory, with a focus on its use in practical applications. The Applied Machine Learning course teaches you a wide-ranging set of techniques of supervised and unsupervised machine learning approaches using R as the programming language. During the course, a selection of topics will be covered in supervised learning, such as linear models for regression and classification, or nonlinear models such as neural networks, and in unsupervised learning such as clustering. The uses and limitations of these algorithms will be discussed, and their implementation will be investigated in programming assignments. The course also covers theoretical concepts such as inductive bias, the PAC and Mistake-bound learning frameworks, minimum description length principle, and Ockham’s Razor.
There will be a strong emphasis on the real-world context in which machine learning systems are used. The use of machine learning components in practical applications will be exemplified, and Public health realistic scenarios will be studied in application areas such as hospitalization metrics using electronic medical record data, clinical trials, natural language processing, image processing, and bioinformatics. The importance of the design and selection of features, and their reliability, will be discussed. In order to ground these methods the course includes some programming and involvement in a semester-long research project. This is a programming course: you will be required to write code.
Other Course Information
- Prerequisite: BIOS 500, BIOS 544 (or BIOS 545) or permission of instructor.
- Click here to view a copy of the syllabus!
- Meeting time: Tuesdays from 10:10am-12:00pm