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Courses - Spring 2024
Machine Learning
Open Seats as of
02/20/2024 at 10:30 PM
Introduction to Optimization
Credits: 3
Grad Meth: Reg
Prerequisite: Undergraduate courses in calculus and basic linear algebra.
Recommended: DATA601.
Focuses on recognizing, solving, and analyzing optimization problems. Linear algebra overview: vector spaces and matrices, linear transformations, matrix algebra, projections, similarity transformations, norms, eigen-decomposition and SVD. Convex sets, convex functions, duality theory and optimality conditions. Unconstrained optimization: 1D search, steepest descent, Newton's method, conjugate gradient method, DFP and BFGS methods, stochastic gradient descent. Constrained optimization: projected gradient methods, linear programming, quadratic programming, penalty functions, and interior-point methods. Global search methods: simulated annealing, genetic algorithms, particle swarm optimization.