| |
Jan 05, 2025
|
|
|
|
|
CSCI 6100 - Machine Learning from Data
Introduction to the theory, algorithms, and applications of machine learning (supervised, reinforcement, and unsupervised) from data: What is learning? Is learning feasible? How can it be done? How can it be done well? The course offers a mix of theory, technique, and application with additional selected topics chosen from Pattern Recognition, Decision Trees, Neural Networks, RBF’s, Bayesian Learning, PAC Learning, Support Vector Machines, Gaussian processes, and Hidden Markov Models.
Prerequisites/Corequisites: Prerequisites: CSCI 2300 ; an advanced 4000-level algorithms-based CSCI or MATH course; familiarity with probability, linear algebra, and calculus.
When Offered: Fall term annually.
Co-Listed: CSCI 4100 . Students cannot receive credit for both CSCI 4100 and CSCI 6100.
Credit Hours: 4
Add to Portfolio (opens a new window)
|
|
