Optimization for Information Systems
(3-0-0-3)
CMPE Degree: This course is Selected Elective for the CMPE degree.
EE Degree: This course is Selected Elective for the EE degree.
Lab Hours: 0 supervised lab hours and 0 unsupervised lab hours.
Technical Interest Groups / Course Categories: Threads / ECE Electives
Course Coordinator: Pan Li
Prerequisites: (CS 1301 [min D] or CS 1371 [min D]) and (MATH 2550 [min D] or MATH 2551 [min D] or MATH 2X51 [min T])
Catalog Description
An introduction to the fundamentals of optimization with a focus on algorithms and applications in signal processing, control systems, machine learning, and robotics.Textbook(s)
Course Outcomes
Formulate inference problems in the language of linear algebra and optimization.
Analyze and compute the solutions to least-squares problems in the context of regression.
Implement and use basic computational methods for solving optimization problems.
Map descriptions of real-world problems into quantitative computational problems.
Strategic Performance Indicators (SPIs)
N/A
Topic List
- Regression and least squares
- First-order and second-order conditions for optimality
- Gradient descent algorithms and accelerated methods
- Neural networks and back propagation
- Constrained optimization and Lagrange duality
- Calculus of variations and principle of least action
- Markov decision processes
- Dynamic programming and reinforcement learning
- Optimal control theory
- The role of convexity
- A taxonomy of convex optimization problems