| Course title | Optimization |
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| Course code | KI/EOTT |
| Organizational form of instruction | Lecture + Lesson |
| Level of course | Master |
| Year of study | not specified |
| Semester | Summer |
| Number of ECTS credits | 7 |
| Language of instruction | English |
| Status of course | unspecified |
| Form of instruction | Face-to-face |
| Work placements | This is not an internship |
| Recommended optional programme components | None |
| Course availability | The course is available to visiting students |
| Lecturer(s) |
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| Course content |
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1. Optimization problems and common tasks: free and constrained optimizations, discrete vs continuous problems, multicriterial optimization, examples. 2. The computing of derivatives and gradients: numerical and symbolical derivative, automatic differentiation. 3. Minimization in 1D: (quadratic interpolation method, golden cut method, Fibonacci numbers method) 4 - 5. First order methods: gradient method, conjugate gradient method, Nesterov type method, Adagrad and RMS method. 6. Second order methods: Newton method, Quasi-Newton methods 7. The least squares method: formulation (curve fitting, regression), linear, nonlinear, Levenberg-Marquardt algorithm. 8. Non-derivative methods: method of Hook-Jeeves, Powell's and Nelder-Mead method 9 - 10. Basic principles of stochastic and population method: simulated annealing, particle swarm method, firefly method, cuckoo method 11-12. Constrained problems: Lagrange multiplier method, KKT conditions, duality, principles of penalty methods 13-14. Quadratic programming: formulation, principles of solutions and selected application - SVM
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| Learning activities and teaching methods |
| unspecified |
| Learning outcomes |
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This course provides an overview of selected optimization techniques. We emphasise continuous optimization methods and methods used in machine learning algorithms and neural networks. An integral part of the course is also own implementation of algorithms and solving practical problems using appropriate software.
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| Prerequisites |
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unspecified
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| Assessment methods and criteria |
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unspecified
Prerequisites: Linear algebra and differential calculus |
| Recommended literature |
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| Study plans that include the course |
| Faculty | Study plan (Version) | Category of Branch/Specialization | Recommended semester |
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