1. Mathematical Modeling and Technology. 2. Modeling with Discrete Dynamical Systems. 3. Differential Equations in the Modeling Perception. 4. Regression Methods and Models. 5. Linear Integer Programming. 6. Mixed Integer Programming. 7. Single Variable and Multivariable Optimization. 8. Nonlinear Optimization Methods. 9. Simulation Models. 10. Model based on the Decision Making. 11. Multi-Attribute Decision Modeling. 12. Mathematical Models in Machine Learning. 13. Application of Mathematical Models in the Deep Learning Model. 14. Optimization in the Machine Learning Models.