Figure 1: Image created using the Midjourney image generation tool
Welcome to the EDS 232 website
Course description
Machine learning is a field of inquiry devoted to understanding and building methods that ‘learn’, that is, methods that leverage data to improve performance on some set of tasks. In this course, we focus on the core concepts of machine learning that beginning ML researchers must know. We cover ‘classical machine learning’ primarily using R, and explore applications to environmental science. To understand broader concepts of artificial intelligence or deep learning, a strong fundamental knowledge of machine learning is indispensable.
Teaching team
Instructor: Mateo Robbins (mjrobbins@ucsb.edu)
- Student hours:
Teaching assistant:
- Student hours:
Important links
- Link to full course syllabus
Weekly course schedule
Lecture: TTh 9:30am - 10:45am PST (Bren 1424)
Section:
Learning objectives
The goal of EDS 232 is to equip students with a strong foundation in the core concepts of machine learning. By the end of the course, students should be able to:
Build machine learning models in Python using popular machine learning packages
Build and train supervised machine learning models for prediction and binary classification tasks, including linear and logistic regression.
Apply best practices for machine learning development so that your models generalize to data and tasks in the real world.
Build and use decision trees and tree ensemble methods, including random forests and boosted trees.
Use unsupervised learning techniques including clustering.
Course requirements
Computing
Textbook
Tentative topics
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