COSC 3 Introduction to Machine Learning

This course introduces machine learning by applying it to real data and tasks. It focuses on the sequence of design decisions involved in solving practical machine learning problems: how raw data are transformed into usable representations, how to select appropriate models based on the problem and other constraints, how the model’s parameters are efficiently learned from data, and how its performance is evaluated. Students will work across multiple data modalities (including audio, images, and text) using progressively more expressive models, from linear methods to neural networks, primarily through hands-on experimentation. By the end of the course, students will be able to design and justify machine learning solutions for new applied problems.

Degree Requirement Attributes

Dist:TLA

The Timetable of Class Meetings contains the most up-to-date information about a course. It includes not only the meeting time and instructor, but also its official distributive and/or world culture designation. This information supersedes any information you may see elsewhere, to include what may appear in this ORC/Catalog or on a department/program website. Note that course attributes may change term to term therefore those in effect are those (only) during the term in which you enroll in the course.

Department-Specific Course Categories

Computer Science