ASE-4046 Optimisation and Data Analysis, 5 cr


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


Robert Piche


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.


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 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   


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.


Opintojakso Vastaa opintojaksoa  Selite 
ASE-4046 Optimisation and Data Analysis, 5 cr MAT-61806 Optimisation and Statistical Data Analysis, 5 cr  

Päivittäjä: Piche Robert, 02.11.2018