Machine Learning Capstone - CS4824
Spring 2026
Instructor
Description
This course will cover machine learning science, including foundations, analysis, and applications of machine learning methods.
Learning objectives
A student who completes this class should (1) have a better understanding of machine learning, and (2) be able to leverage machine learning to solve real-world problems. In particular:
Understanding of ML
- Familiarity with a breadth of foundational machine learning concepts.
- Awareness of the mathematical and computer science concepts underlying machine learning.
- Acquiring background knowledge to be able to understand new machine learning methods not covered in the course.
Application of ML
- Ability to formulate real-world problems into machine learning tasks.
- Ability to make informed decisions about which machine learning methods are appropriate for different tasks.
- Ability to implement standard machine learning methods without using prepackaged machine learning software.
Topics
Supervised learning, unsupervised learning, and best practices in machine learning.
Prerequisites
- Experience in Python, data structures, algorithms, calculus, linear algebra, probability, and statistics.
- A grade of C or better in CS 3654 and a C or better in CS 3114.
- Experience in Python, data structures, algorithms, and statistics.
