ASE-4046 Optimisation and Data Analysis, 5 cr

Lisätiedot

Acceptable for postgraduate studies only if grade is at least 3.
Suitable for postgraduate studies.

Vastuuhenkilö

Robert Piche

Opetus

Osaamistavoitteet

A hands-on introduction to optimisation and statistical modelling. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Period 3: Optimisation %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 1. Computer arithmetic - floating point numbers - FP arithmetic 2. Linear programming - examples: production planning, transportation allocation, diet planning - graphical solution - Matlab LINPROG - ill-posed LP problems 3. Curve fitting - fitting a line and fitting a polynomial - data linearisation transformations - robust fit with linear programming 4. Nonlinear least squares - examples: positioning, curve fitting, feedback controller design - Gauss-Newton method - LSQNONLIN - ill-conditioned problems 5. Nonlinear optimisation - unconstrained, FMINUNC - Lagrange multipliers - quadratic cost with linear equality constraints - FMINCON 6. Multiobjective optimisation - Pareto optimality - weighted sum method - goal attainment, FGOALATTAIN - examples: shopping, controller design %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Period 4: Statistical data analysis %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 7. Visualising data - histogram, kernel PDF, CDF - medians, quantiles, box plots - data presentation do's and don'ts 8. Inference of categories - frequency diagram - Bayes formula - Bayesian nets, AISPACE software 9. Inference of Bernoulli parameter - binomial sampling model - posterior distribution and predictive distribution - updating: using prior information, sequential learning - parameter difference via Monte Carlo 10. Inference of Gaussian mean - Gaussian sampling model, normal QQ plot - posterior distribution & predictive distribution - updating: using prior information, sequential learning 11. Multiple linear regression - examples: fitting a line, fitting a polynomial - posterior distribution & predictive distribution - goodness of fit - updating: sequential learning 12. Kalman filter - state space (hidden Markov) model - Bayes filter - Kalman filter, steady-state KF, extended KF - examples: channel estimation, target tracking

Esitietovaatimukset

Tietoa esitietovaatimuksista
Multivariate calculus, probability, and basic Matlab programming skills are mandatory prerequisites.

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