In almost every field, there is a need to draw inferences from or make decisions based on data. The goal of this course is to introduce machine learning that is approachable to diverse disciplines and empowers students to become proficient in the foundational concepts and tools. You will learn to (a) structure a machine learning problem and determine which algorithmic tools are appropriate, (b) evaluate the performance of your solution using field-appropriate metrics and practices, and (c) accurately interpret your model output and communicate your results to interdisciplinary audiences. This course is a fast-paced, applied introduction to machine learning that through extensive practice with foundational tools, helps you to develop strengths in your knowledge of foundational machine learning concepts and provides practical experience with those tools to prepare you for practice or future study.
IDS 705: Principles of Machine Learning