ASE-4046 Optimisation and Data Analysis, 5 cr
Lisätiedot
Acceptable for postgraduate studies if grade is at least 3.
Suitable for postgraduate studies.
Vastuuhenkilö
Robert Piche
Opetus
| Toteutuskerta | Periodi | Vastuuhenkilö | Suoritusvaatimukset |
| ASE-4046 2018-01 | 3 - 4 |
Mostafa Mansour Robert Piche Jaakko Pihlajasalo |
There is no final exam. The grade is based on weekly quizzes in the EXAM system. Bonus points are given for participation in weekly exercise sessions. |
Osaamistavoitteet
After completing the course, the student has knowledge of optimisation and statistical problems, methods, and software, and is able to use software to model and solve practical problems.
Sisältö
| Sisältö | Ydinsisältö | Täydentävä tietämys | Erityistietämys |
| 1. | Computer arithmetic: floating point numbers; FP arithmetic | ||
| 2. | Linear programming: LP problems in production planning, transportation allocation, and diet planning; solving them with Matlab LINPROG | ill-posed LP problems | |
| 3. | Curve fitting: least squares fit of a line and of a polynomial; data linearisation transformations | robust curve fitting using linear programming | |
| 4. | Nonlinear least squares: problems in positioning, curve fitting, and feedback controller design; solution with Matlab LSQNONLIN | Gauss-Newton method; ill-conditioned problems | |
| 5. | Nonlinear optimisation: unconstrained problems and solution with FMINUNC; Lagrange multipliers; solution with FMINCON | quadratic cost with linear equality constraints | |
| 6. | Multiobjective optimisation: Pareto optimality; weighted sum method; goal attainment with FGOALATTAIN | Feedback controller design as a MOO problem. | |
| 7. | Visualising data: histogram, CDF, medians, quantiles, box plots, data graphics do's and don'ts | kernel smoothing with KSDENSITY | |
| 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; using prior information; sequential learning | parameter difference via Monte Carlo | |
| 10. | Inference of Gaussian mean: Gaussian sampling model; posterior distribution & predictive distribution; using prior information; sequential learning | normal QQ plot | |
| 11. | Multiple linear regression: fitting a line; posterior distribution & predictive distribution; sequential learning | fitting a polynomial; goodness of fit | |
| 12. | Filtering: state space model, Kalman filter, steady-state KF, target tracking | Bayes filter; channel estimation |
Esitietovaatimukset
| Opintojakso | P/S | Selite |
| MAT-01266 Mathematics 2 | Mandatory | |
| MAT-01466 Mathematics 4 | Mandatory | |
| MAT-02506 Probability Calculus | Mandatory |
Tietoa esitietovaatimuksista
Mandatory prerequisites: Multivariate calculus, probability, and basic Matlab programming skills.
Vastaavuudet
| Opintojakso | Vastaa opintojaksoa | Selite |
| ASE-4046 Optimisation and Data Analysis, 5 cr | MAT-61806 Optimisation and Statistical Data Analysis, 5 cr |