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Meetings: TueThu 17:00-18:15 in 206 Robinson Hall
- NEWS Dec.-10: Posted new lecture notes on wavelet analyses for Dec.-9 class period.
- NEWS Dec.-8: Posted new lecture notes on non-stationary data analyses for Dec.-2 and Dec.-7 class periods.
- NEWS Sept.-16: Only Section-1 of Homework due Sept.-23, Section-2 is due Sept.-30.
- NEWS Sept.-7: Updated Class Web-Site
Instructor: Andreas Münchow
Goal: Provide each student with a set of tools to confidently handle data analysis tasks in both time (space) and frequency (wave number) domains.
Format: This is a traditional lecture style course, however, most learning will take place via a set of computer projects that translate lecture materials into tested code.
Expectations: Each student shall be able to develop, apply, and critically evaluate
- Auto-spectral analyses
- Digital filters
- Linear systems
- Least-squares function fitting, and
- Empirical orthogonal functions
Grading: 80% for problem-based projects, 10% homeworks, 10% individual in-class participation
Exam: None, but I may use occasional quizzes related to prior lectures (always open book).
Last updated: Fri Dec 10 08:09:14 EST 2021