MATH-5544: Mathematical Optimization for Machine Learning
Description: Formulation and analyses of mathematical problems to minimize or maximize functions. Convex, nonlinear, constrained, largescale, and stochastic optimization problems. Gradient based and higher order optimization methods. Convergence analysis and implementation of optimization strategies. Accuracy and performance trade-offs. Introduction to neural networks, backpropagation, supervised learning, and machine and deep learning strategies, including stochastic approximation and sample average approximation. Regression and classification applications. Pre: Graduate standing.
Pathways: N/A
Course Hours: 3 credits
Corequisites: N/A
Crosslist: N/A
Repeatability: N/A
Sections Taught: 1
Average GPA: 3.87 (A)
Strict A Rate (No A-) : 89.50%
Average Withdrawal Rate: 0.00%
| Sturler Eric De | 2023 | 89.5% | 5.3% | 5.3% | 0.0% | 0.0% | 0.0% | 3.87 | 1 |