ASE-4046 Optimisation and Data Analysis, 5 cr
Additional information
Acceptable for postgraduate studies if grade is at least 3.
Suitable for postgraduate studies.
Person responsible
Robert Piche
Lessons
| Implementation | Period | Person responsible | Requirements |
| 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. |
Learning Outcomes
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.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 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 |
Prerequisites
| Course | Mandatory/Advisable | Description |
| MAT-01266 Mathematics 2 | Mandatory | |
| MAT-01466 Mathematics 4 | Mandatory | |
| MAT-02506 Probability Calculus | Mandatory |
Additional information about prerequisites
Mandatory prerequisites: Multivariate calculus, probability, and basic Matlab programming skills.
Correspondence of content
| Course | Corresponds course | Description |
| ASE-4046 Optimisation and Data Analysis, 5 cr | MAT-61806 Optimisation and Statistical Data Analysis, 5 cr |