№ lp_2_3_28170
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University course syllabus outlining structure, topics, prerequisites, grading policy, and schedule for a two-part graduate-level class on computationally inspired statistical methods and statistical inference via convex optimization.
Course Code: ISYE 8813
Course Title: Special Topics in Data Science
Time/Place: TuTh 1:35–2:55, IC 107
Instructors: Arkadi Nemirovski; Jeff Wu
Institution: Georgia Institute of Technology
Structure: Two modules taught separately but intellectually connected
Module I Title: Computationally Inspired Statistical Methods
Module I Instructor: Jeff Wu
Module I Assessment: Attendance and participation; reports on assigned papers; final in-class presentation or written report; no midterm
Module I Deadline: All required work due by Tu Feb 28
Module II Start Date: March 2
Module II Title: Statistical Inferences via Convex Optimization
Module II Instructor: Arkadi Nemirovski
Module II Assessment: Final exam only; no homework or midterm
Grading Policy: Final grade = min(max(grade1, grade2), min(grade1, grade2)+1)
Prerequisites Module I: Basic mathematical statistics; regression; statistical computing; master’s level in statistics or OR
Prerequisites Module II: Elementary linear algebra; analysis; basic mathematical culture
Related Material: Lecture Notes by A. Juditsky and A. Nemirovski, Statistical Inferences via Convex Optimization
Price: 8 / 10 USD
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