DATA 505: Applied Machine Learning

Spring 2025, Willamette University

MW 6:00 - 10:00 PM Salem, Ford 102

Syllabus

Instructor

Calvin Deutschbein

ckdeutschbein@willamette.edu

Ford 3rd Floor

Course Description

Machine learning is becoming a core component of many modern organizational processes. It is a growing field at the intersection of computer science and statistics focused on finding patterns in data. Prominent applications include personalized recommendations, image processing and speech recognition. This course will focus on the application of existing machine learning libraries to practical problems faced by organizations. Through lectures, cases and programming projects, students will learn how to use machine learning to solve real world problems, run evaluations and interpret their results.

Core Learning Objectives

1. Understand and apply the notion statistical error to machine learning applications.
2. Systematically approach trade-offs in accuracy and utility of algorithms.
3. Proactively consider data leakage and non-independence in analysis.
4. Leverage feature engineering in ML, and beyond.

Week	Date	Slides	Videos	Code	Homework
1	Jan 13
2	Jan 20	King Day
3	Jan 27
4	Feb 3
5	Feb 10
6	Feb 17
7	Feb 24
8	Mar 3	Midterm
9	Mar 10
10	Mar 17
11	Mar 24	Spring Break
12	Apr 7
13	Apr 14
14	Apr 21
15	Apr 28	Final Presentations