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Teaching GuideTerm Faculty of Computer Science |
| Grao en Ciencia e Enxeñaría de Datos |
 Subjects |
| Numerical Methods for Data Science |
| Contents |
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| Identifying Data | 2023/24 | |||||||||||||
| Subject | Numerical Methods for Data Science | Code | 614G02033 | |||||||||||
| Study programme |
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| Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
| Graduate | 1st four-month period |
Fourth | Optional | 6 | ||||||||||
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| Topic | Sub-topic |
| Basic concepts in numerical methods: convergence, errors and order. | |
| Numerical methods for nonlinear equations | Bisection, secant method, Regula Falsilla, fixed point method and Newton-Raphson |
| Numerical methods for the solution of large linear systems | Direct and iterative methods |
| Numerical methods for approximating eigenvalues and eigenvectors | Power methods. QR method. |
| Methods for storing large matrices in the computer | |
| Numerical methods for solving nonlinear systems of equations | Fixed point methods. Newton's method. |
| Numerical methods for optimization | Gradient and Conjugate gradient methods. Line-search methods. Newton and quasi-Newton methods. Global optimization methods and two-phase methods. |
| Numerical interpolation in one and several variables. |
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