| 1 | Artificial Intelligence as a group of methods that adapt their behavior. Turing test. Modeling and optimization as main tools to attain the adaptation goal. | |
| 2 | Modeling - task definition, parameter models, models to approximate functions, regression and classification tasks. Linear regression - basic facts. Residue function and the error. Training and testing sets. | |
| 3 | Linear regression - application in time series modeling. Nonlinear regression. Multilayer perceptron. | |
| 4 | Support Vector Machines. | |
| 5 | Uncertainty and modeling. Fuzzy models, simple rule based fuzzy model, Takagi-Sugeno model. | |
| 6 | Aggregating models, meta-learning. Crossvalidation and bootstrapping. | |
| 7 | Test. | |
| 8 | Building decision trees. ID3 algorithm and methods to improve trees. Bagging and boosting. Random forests. | |
| 9 | Cluster analysis: LVQ method, Kohonen algorithms, c-means and fuzzy c-means. | |
| 10 | Reinforcement learning. | |
| 11 | Metaheuristics in the optimization task, simulated annealing method. | |
| 12 | Ewolutionary method. | |
| 13 | Ant colony optimization, particle swarm optimization. | |
| 14 | artificial immune systems. | 15 | Summary and outlook. Test. |