CS-5805: Machine Learning
Description: 5805: Provides an introduction to the field of machine learning (or data mining) and explores the common tasks of machine learning to include preprocessing, classification, clustering, the discovery of association rules/sequential patterns, and anomaly detection. Introduces fundamentals of probability theory and random variables for classification and clustering. Investigates multiple linear and nonlinear regression models to classify social phenomena. Apply application of machine learning in solving real work problems. Credit will not be given for both CS/STAT 5525 and CS 5805. Pre: Graduate standing. 5806: Provides an in-depth understanding of classical machine learning theory using Bayesian models, statistical machine learning and pattern recognition techniques, advanced machine learning methods, and their applications.
Pathways: N/A
Course Hours: 3 credits
Corequisites: N/A
Crosslist: N/A
Repeatability: N/A
Sections Taught: 7
Average GPA: 3.53 (rounds to A-)
Strict A Rate (No A-) : 46.33%
Average Withdrawal Rate: 0.00%
| Reza Jafari | 2024 | 58.9% | 31.3% | 7.6% | 0.9% | 1.3% | 0.0% | 3.41 | 4 |
| Martin Skarzynski | 2023 | 96.9% | 1.6% | 0.0% | 0.0% | 1.6% | 0.0% | 3.90 | 1 |
| Hoda M Eldardiry | 2024 | 78.5% | 19.1% | 2.4% | 0.0% | 0.0% | 0.0% | 3.75 | 1 |
| Anuj Karpatne | 2024 | 39.5% | 58.7% | 0.9% | 0.0% | 0.9% | 0.0% | 3.39 | 1 |