IS 3321 Principles of Optimization and Inference 3.0 Credits
Develops the computational and quantitative techniques needed to develop and implement analytic methods, including model training, statistical inference, approximation, and estimation. Building on foundational calculus and computational skills, this class introduces more complex analytic techniques and explores their quantitative foundations. Demonstrates advanced toolkits in numerical computing and introduces techniques for statistical inference and model optimization used in learning applications. Presents foundational knowledge on the definition and function of analytic processes that underlie prediction, measurement, and optimization. Course provides experience understanding numerical representation effects and introduces parameterization in relation to inference and data analysis.
Repeat Status: Not repeatable for credit
Prerequisites: (IS 1310 [Min Grade: D] or BMES 1610 [Min Grade: D] or INFO 212 [Min Grade: D] or BMES 201 [Min Grade: D]) and (IS 2320 [Min Grade: D] or MATH 2801 [Min Grade: D] or STAT 2201 [Min Grade: D] or PHYS 4801 [Min Grade: D] or MATH 1801 [Min Grade: D] or ECE 3700 [Min Grade: D] or BMES 2110 [Min Grade: D] or INFO 332 [Min Grade: D] or MATH 311 [Min Grade: D] or STAT 202 [Min Grade: D] or PHYS 440 [Min Grade: D] or MATH 410 [Min Grade: D] or ECE 361 [Min Grade: D] or BMES 310 [Min Grade: D]) and (MATH 1002 [Min Grade: D] or MATH 1201 [Min Grade: D] or MATH 102 [Min Grade: D] or MATH 121 [Min Grade: D]) and (MATH 2401 [Min Grade: D] or MATH 2901 [Min Grade: D] or MATH 201 [Min Grade: D] or MATH 261 [Min Grade: D])