Algorithmics and data structures 3

This module will enable students to develop some fundamental algorithms in computer science and, additionally, to work with more advanced data structures

Introduction to Information Systems (IIS)

The objective of this module is to present the role and importance of IS in organizations, while allowing students to acquire basic skills necessary for the analysis and design of management information systems. The module begins with an understanding of the organizational and functional structures of the company, followed by the study of the properties, the role of information and the techniques of its codification and control. Subsequently, the methodological tools and the approach of analysis and design of IS will be presented.

After studying this subject, the student had to demonstrate the following skills:

- Be able to solve the problems posed by companies through the good understanding of the IS.

- Be able to analyze, decompose, model and execute a business project as an IS problem to be solved.

Numerical Methods

This course provides an in-depth exploration of numerical methods, emphasizing their theoretical foundations and practical applications in solving mathematical problems. Students will engage with key concepts in floating point arithmetic, linear system solving, and the computation of eigenvalues and eigenvectors.

Key Topics Include:

1. Floating Point Arithmetic:

  • Understanding the representation of real numbers in computers, including precision and accuracy issues.
  • Analysis of rounding errors, truncation errors, and their impact on numerical computations.

2. Solving Linear Systems:

  • Examination of direct and iterative methods for solving linear equations.
  • Implementation of algorithms such as Gaussian elimination, LU decomposition, and the Jacobi and Gauss-Seidel methods.
  • Exploration of matrix factorization techniques and their computational efficiency.

3. Eigenvalues and Eigenvectors:

  • Introduction to the concepts of eigenvalues and eigenvectors in linear algebra.
  • Methods for computing eigenvalues and eigenvectors, including the Power Method and QR algorithm.

Learning Outcomes:

By the end of the course, students will be able to:

  • Understand and apply floating point arithmetic in computational settings.
  • Solve linear systems using both direct and iterative numerical methods.
  • Compute and interpret eigenvalues and eigenvectors.

This course is ideal for students seeking a comprehensive understanding of numerical methods and their relevance in scientific computing, engineering, and applied mathematics. Practical programming assignments will reinforce theoretical knowledge, enabling students to implement numerical algorithms effectively.

Computer Architecture

Course Objectives

The MIPS architecture is an excellent choice for teaching RISC architecture concepts. Thanks to its simplicity, clear structure, and the availability of educational simulators like MARS, MIPS allows students to develop a solid understanding of processors and instruction sets.

Towards the end of this course, students should be able to:

  • -        Describe the instruction set architecture of a MIPS processor
  • -        Analyze, write, and test MIPS assembly language programs
  • -        Describe the organization/operation of integer and floating-point arithmetic units
  • -        Design the datapath and control of a single-cycle processor
  • -        Design the datapath and control of a pipelined processor and handle hazards
  • -        Describe the organization/operation of memory and caches
  • -        Analyze the performance of processors and caches


Mathematical Logic