46-886 Machine Learning Fundamentals
6 units, Carnegie Mellon University
The most important emergent theme in business over the last ten years can be described in one word: data. As firms across all industries continue their respective arms races to collect increasing amounts of increasingly-complex data, there is accelerating demand for analysts, consultants, and scientists with the ability to transform that data into actionable models and insights. To that end, the goal of this course is to introduce students to the machine learning tools and software that drive modern predictive analytics in business settings. In this course,students will: + Gain an understanding of the cross-cutting principles driving the design and deployment of modern machine learning algorithms. + Learn about the inner workings of the most popular machine learning algorithms including lasso and ridge regression, decision trees, random forests, boosted trees, nearest neighbors, clustering and neural networks, and understand the flexibility afforded by each. + Learn to implement all of these algorithms on real data drawn from prototypical uses of these algorithms in real-world applications, ranging from finance, marketing, operations, healthcare, and more. The course will utilize Python, a free programming language used ubiquitously in modern analytics ecosystems. + Evaluate the merits of each algorithmic modeling approach, and list potential pitfalls.
Prerequisites: none listed
Offered: Spring 2027, Fall 2026, Spring 2026, Fall 2025
Courseweave needs JavaScript to show sections, workloads and planning tools.