Lecture Description
By the end of the course, students will be able to:
- Distinguish between random, systematic, and gross errors (outliers).
- Understand the general law of error propagation and apply it to various practical cases.
- Understand the principle of the method of least squares and explain its widespread use in practice.
- Be familiar with the concepts of indirect and conditional least-squares adjustments, distinguish between them, and combine them as appropriate.
- Are able to use the fundamental features of the Python programming language independently.
- Are able to implement and solve both linear and non-linear adjustment problems using Python or another programming language.
- Are familiar with common data filtering methods and can implement them independently in Python or another programming language.
- Are able to evaluate the results of a least-squares adjustment using appropriate statistical methods.
- Place Fourier analysis in the context of least-squares adjustment and understand its significance for data filtering.
- Understand the transition from sequential adjustment methods to the Kalman optimal filter.
