Course Objectives:
The course is intended to introduce the students the techniques of optimization in Engineering Design. The course will in introduce
- Principle of optimization, formulation of objective function design constraints and classification of optimization problem.
- Optimization techniques such as Single variable and multivariable optimization, multi objective optimization
- Static and dynamic applications where the optimization techniques will be used.
- Teacher: Post-Graduate Teacher215
The course aims to give a concise introduction to non-linear Kalman filtering and smoothing, particle filtering and smoothing, and to the related parameter estimation methods. The mathematical treatment of the models and algorithms in this course is Bayesian, which means that all the results will be treated as being approximations to certain probability distributions or their parameters. Probability distributions will be used both to represent uncertainties in the models and for modeling physical randomness. The theories of non-linear filtering, smoothing, and parameter estimation will be formulated in terms of Bayesian inference, and both the classical and recent algorithms will be derived using the same Bayesian notation and formalism.
- Teacher: Dr. Abdu Mohammed
- Teacher: Post-Graduate Teacher213