## ALGEBRA

### Course

Code: 2200

Degree: Bachelor's in Electrical Engineering

School of Engineering of Elche

Year: Year 1 of Bachelor's in Electrical Engineering

Semester: Fall

Type: Core

Language: Spanish

ECTS credits: 6 Lecture: 3 Laboratory: 3 | Hours: 150 Directed: 60 Shared: 15 Autonomous: 75 |

Subject matter: Mathematics

Module: Core

Department: Statistics, Mathematics and Informatics

Area: APPLIED MATHEMATICS

Course instructors are responsible for the course content descriptions in English.

### Description

### Faculty

Name | Coordinator | Lecture | Laboratory |
---|---|---|---|

FERRANDO PEREZ, JUAN CARLOS | ■ | ■ | ■ |

### Competencies and learning outcomes

#### General competencies

- Knowledge of basic materials and technologies to enable learning new methods and theories providing versatility for adapting to new situations.

#### Specific competencies

- Skills for solving mathematical problems that may arise in engineering. Aptitude for applying knowledge towards linear algebra, geometry, differential geometry, differential and integral calculus, differential equations and partial derivatives, numerical and numerical algorithmic methods, statistics, and optimization.

#### Objectives (Learning outcomes)

- 01Know the different kinds of matrices and its properties. Perform operations with vectors and matrices.
- 02Students will be able to use matrix techniques to represent and solve a system of simultaneous linear equations checking its nature previously
- 03Know and find the fundamental subespaces of a matrix.
- 04Understand the extension of vector concepts to abstract vector spaces of arbitrary finite dimension.
- 05Understand linear transformations, theirs matrix representations and applications.
- 06Understand the metric concepts of inner product, norm, orthogonality and orthogonal projection.
- 07Find and interpret the eigenvalues and eigenvectors of a linear transformation.
- 08Identify when a matrix is diagonalizable and find its diagonal form.
- 09Apply spectral techniques to solve engineering problems.

### Contents

#### Teaching units

#### Association between objectives and units

Objective/Unit | U1 | U2 | U3 | U4 | U5 |
---|---|---|---|---|---|

01 | |||||

02 | |||||

03 | |||||

04 | |||||

05 | |||||

06 | |||||

07 | |||||

08 | |||||

09 |

#### Schedule

Week | Teaching units | Directed hours | Shared hours | Autonomous hours | Total hours |
---|---|---|---|---|---|

1 | U1 | 2 | 0 | 8 | 10 |

2 | U1 | 4 | 0 | 6 | 10 |

3 | U1 | 2 | 0 | 8 | 10 |

4 | U2 | 6 | 2 | 2 | 10 |

5 | U2 | 4 | 0 | 6 | 10 |

6 | U2 | 4 | 2 | 4 | 10 |

7 | U2,U3 | 6 | 1 | 3 | 10 |

8 | U3 | 4 | 2 | 4 | 10 |

9 | U3 | 6 | 0 | 4 | 10 |

10 | U4 | 4 | 2 | 4 | 10 |

11 | U4 | 2 | 0 | 8 | 10 |

12 | U4 | 6 | 2 | 2 | 10 |

13 | U5 | 2 | 0 | 8 | 10 |

14 | U5 | 6 | 0 | 4 | 10 |

15 | U5 | 2 | 4 | 4 | 10 |

#### Basic bibliography

- Arvesú Carballo, Jorge. Marcellán Español, Francisco / Sánchez Ruiz, Jorge. "Problemas resueltos de álgebra lineal". Madrid Thomson-Paraninfo D.L. 2005.
- Aversú Carballo, Jorge. Alvarez Nodarse, Renato / Marcellán Español, Francisco. "Algebra lineal y aplicaciones". Madrid Síntesis 1999.
- Barbolla, Rosa. Sanz, Paloma. "Álgebra lineal y teoría de matrices". Madrid [etc.] Prentice Hall 1998.
- Burgos Román, Juan de. "Álgebra lineal". Madrid [etc.] McGraw Hill D.L.1996.
- Kolman, Bernard. Hill, David Ross , col. "Algebra lineal con aplicaciones y matlab". México [etc.] Prentice Hall 1999.
- Merino González, Luis M. Santos Aláez, Evangelina , coaut. "Álgebra lineal con métodos elementales". Granada [.s.n.] D. L. 1999.

#### Complementary bibliography

- Hill, David R. Zitarelli, David E. "Linear algebra labs with MATLAB". Upper Saddle River Prentice Hall [1996].
- McMahon, David (David M.). "Linear algebra demystified [electronic resource] /". New York : McGraw-Hill, c2006.
- McMahon, David (David M.). "MATLAB demystified [electronic resource] /". New York : McGraw-Hill, c2007.

#### Links

#### Software

- Matlab R2011b

### Methodology and grading

#### Methodology

**Lecture:**Pass on knowledge and activate cognitive processes in students, encouraging their participation.**Solving exercises and problems:**Exercise, test, and apply previous knowledge through routine repetition.

#### Grading

The final mark will be calculated according to the following items: 1) Test type exams (10%) 2) MatLab laboratory sessions (10%) 3) Final exam (75%) 4) Tutorships (5%) In order to pass the subject are required two conditions: 1) Obtain more than 4 points in the final exam. 2) Obtain a weighted average mark equal or higher than 5. For september and december exam, the evaluation is: 1) Final exam (75%) 2) MatLab exam (10%) 3) Test type exam (10%)

#### Assessment test characteristics

Objective theoretical-practical examination

#### Correction criteria

Individual exercise scores appear explicitly in the exam model