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Teaching GuideTerm Faculty of Computer Science |
| Grao en Ciencia e Enxeñaría de Datos |
 Subjects |
| Numerical Methods for Data Science |
| Contents |
| Identifying Data | 2022/23 | |||||||||||||
| 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 matrix methods in high dimensions. | 1. Storage of large matrices. 2. Direct and iterative methods for solving large linear systems of equations. 3. Numerical approximations of eigenvalues of large matrices. |
| Numerical methods to solve nonlinear equations and nonlinear systems of equations. | 1. Numerical methods for nonlinear equations: bisection, secant, regula-falsi, fixed-point and Newton-Raphson. 2. Numerical methods for large systems of nonlinear equations: fixed point and Newton. |
| Numerical methods for optimization of large problems. | 1. Gradient and Conjugate gradient methods. 2. Line-search methods. 3. Newton and quasi-Newton methods. 4. Global optimization methods and two-phase methods. |
| Numerical interpolation in one and several variables. |
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