Learning results After the end of the teaching module, the trainee will be able to:

1. Identify the representation models and analyze the problem of uncertainty and its forms.
2. Recalls basic knowledge about probability distributions and statistical characteristics.
3. Describes the simulation process and its applications.
4. Import and run model simulation on PC with the Monte Carlo method.
5. Explain the concept of optimization and recognize heuristic and genetic search algorithms.
6. Define the concept of ignorance and its forms and describe learning methods (supervised, unsupervised, reinforcement).
7. Describe the basic modeling process and criteria for selecting models and parameters and define the concept of regression.
8. Designs, implements and uses learning algorithms in various scientific fields.

1. Representation models, uncertainty, statistics, ambiguity
2. Model simulation (Monte Carlo)
3. Optimum search (heuristic and genetic algorithms)
4. Data, knowledge, model ignorance and learning (supervised or unsupervised and reinforcement)
5. Regression (fitting) – Basic modeling
6. Simulation, Monte Carlo, fitting applications

Learning results After the end of the teaching module, the trainee will be able to:

1. Discusses the purpose and key concepts of machine learning, pattern recognition, artificial intelligence and data quality.
2. Define the concept of classification and its various methods, use classification algorithms and evaluate its role in decision making.
3. Describes the clustering process and its role in data correlation.
4. Describes artificial neural networks, their types and their implementations.
5. Recognize the categories of problems and choose the appropriate artificial intelligence method to solve them.
6. Designs and implements algorithms to solve problems in various scientific fields.

1. Machine Learning – Pattern Recognition and Artificial Intelligence
2. Classification (Linear Classification, k-NN, Bayesian, SVM) and decisions
3. Clustering (k-means) and correlation
4. Neural networks for regression, classification and clustering
5. Deep Learning and Convolutional Neural Networks (CNN)
6. Modeling problems
7. Applications of classification, clustering and neural networks

Learning results After the end of the teaching module, the trainee will be able to:

1. Explain the concepts of forecasting and estimation and describe forecasting and estimation models and their characteristics.
2. Specifies the use of Kalman and Lainioti filters.
3. Defines steady state and steady state Kalman filters.
4. Solves the Riccati equation used in steady state Kalman filters.
5. Recognizes non-linear prediction and estimation models and the extended Kalman filter.
6. Design and program Kalman filters to solve problems in various scientific fields.

1. Valuation theory
2. Linear Kalman filter
3. Steady state
4. Linear Lainiotis filter
5. Extended Kalman filter (extended Kalman filter)
6. Applications