EEE461 Optimization

2024-2025 Fall

Week	Part	Topic
1		Introduction to optimization and machine learning
2	Linear algebra and least squares	Linear algebra review
3	Linear algebra and least squares	Least squares (LS) method, Gram-Schmidt algorithm
4	Linear algebra and least squares	LS classification and cross-validation
5	Linear algebra and least squares	Singular value decomposition, principal component analysis
6	Optimization	Fundamentals, multi-objective problems
7	Optimization	Regularization, iterative methods
8		Midterm exam
9	Optimization	Proximal algorithms
10	Optimization	Convex optimization, duality
11	Optimization	Gradient methods, stochastic gradient descent
12	Machine learning	Support vector machine
13	Machine learning	Artificial neural networks and perceptron
14	Machine learning	Deep learning, convolutional neural networks
15	Machine learning	Unsupervised learning, k-means clustering