3 credits
Fall 2025 Lecture Distance Learning Upper Division
This introductory course will cover many concepts, models, and algorithms in machine learning. Topics include classical supervised learning (e.g., regression and classification), unsupervised learning (e.g., principle component analysis and K-means), and recent development in the machine learning field such as variational Bayes, expectation propagation, and Gaussian processes. While this course will give students the basic ideas and intuition behind modern machine learning methods, the underlying theme in the course is probabilistic inference.
Learning Outcomes1Learn the theory and key algorithms used in machine learning.
2Get hands-on machine learning experience by implementing several algorithms, applying them to datasets and analyzing their performance.
3Understand how to use machine learning methods to their research projects, formulate the learning tasks and match them with appropriate solutions.
Restrictions
made by Purdue students.