M4004 - ECTS Upon completion of this curricular unit, the student should:

— master the concepts, methods and results presented, on cryptography and on cryptanalysis, and some of its applications;
— be able to analyze and solve problems of Cryptology, using the methods and results that best apply to the problem under study;
— have adequate preparation to conduct studies and research in areas of mathematics that integrate or use Cryptology;
— be able to communicate, in an efficient manner, his or hers own solutions to problems, and the various topics lectured.

M4038 - ECTS

This course is intended for students to acquire basic knowledge of the theory and numerical treatment of partial differential equations.

M4083 - ECTS

It is expected that at the end of the course the students will attain knowledge on:

a)     a) data collection

b)    b)  most used statistical models in the context of Science and Engineering, 

           including its application with the free software R

c)     c) the choice of the statistical model given different contexts

d)     d) the interpretation of the results obtained by the application of the learnt methods.

M4042 - ECTS

The aim of this curricular unit is to study some of the models, techniques and algorithms more frequently used in other áreas of knowledge. Each technique should be used for resolution of problems arising in other sciences and for establishing mathematical models for such problems. 

M4045 - ECTS

The course aims to introduce n aa rigorous the optimization theory (linear and nonlinear), variational calculus and theory of control. The fundamental concepts of these areas are addressed, as well as the most important mathematical tools for its analysis.

 

M4064 - ECTS

Introduction to stochastic processes.Tools for the analysis of stochastic processes and its applications in several areas, such as signal processing, information theory, finance and economics, biology and medicine. Special attention to the understanding of the concepts and methods and to its application in interdisciplinary areas using simulated and real data.

CC4019 - ECTS

The aim of of information theory is to expose fundamental concepts related to information and its applications in systems and communications networks and computer science.

M4059 - ECTS To introduce the fundamental concepts and principles of risk theory.
To provide a fundamental knowledge of the commonly used stochastic models and techniques in non-life insurance mathematics.

CC4015 - ECTS

Provide the student experience in the use, administration and programming of some of the systems / applications currently used in the Windows environment. The particular focus is on the programming environment of Visual Basic for Applications.

M4031 - ECTS

The course presents the main concepts and techniques of digital image processing and analysis. The main goal is that in the end of the course the students will be able to plan and implement algorithms for information extraction from images.

The course orientation focus on the understanding of concepts and methods, and its effective use in synthetic and experimental data analysis. The course makes an extensive use of advance computational tools (MATLAB).

M4076 - ECTS

It is intended that the students learn the paradigm of computational simulation based on Monte Carlo methods, namely MCMC, as well as the principles of numerical linear algebra, in a framework of critical application as well as their application in interdisciplinary areas involving the social, life or computational sciences.

CC4024 - ECTS Identification and application of data mining techniques to extract knowledge from different data sources (e.g. text, web).

M4063 - ECTS

Introduce the main concepts and methods of supervised and unsupervised classification.

M4015 - ECTS To provide the students with advanced regression techniques for Gaussian, binomial and Poisson regressions, especially designed for repeated measures and longitudinal data.

M4049 - ECTS

The main objective of the course is to introduce in a rigorous way the fundamentals of Game Theory, with particular emphasis on Nash equilibria.

CC4020 - ECTS To improve background on techniques for designing algorithms and analysing their correctness and complexity.
To know and apply methods for finding exact and approximate solutions for hard problems.

M4026 - ECTS

The course will focus on Markov decision processes and some generalizations. Markov decision processes, also referred to as stochastic dynamic programs or stochastic control problems, are models for sequential decision making when outcomes are uncertain. The Markov decision process model consists of decision epochs, states, actions, rewards, and transition probabilities. Choosing an action in a state generates a reward and determines the state at the next decision epoch through a transition probability function. Policies or strategies are prescriptions of which action to choose under any eventuality at every future decision epoch. Decision makers seek policies which are optimal in some sense. An analysis of this model includes

1. providing conditions under which there exist easily implementable optimal policies;
2. determining how to recognize these policies
3. developing and enhancing algorithms for computing them; and
4. establishing convergence of these algorithms.

M5009 - ECTS The objectives are defined by each supervisor and the  student enrolled in the Master's dissertation.

M5007 - ECTS The internship aims to develop the
students' ability to meet the real world
challenges and to promote their professional integration in a wide range of companies.

M4004 - ECTS Upon completion of this curricular unit, the student should:

— master the concepts, methods and results presented, on cryptography and on cryptanalysis, and some of its applications;
— be able to analyze and solve problems of Cryptology, using the methods and results that best apply to the problem under study;
— have adequate preparation to conduct studies and research in areas of mathematics that integrate or use Cryptology;
— be able to communicate, in an efficient manner, his or hers own solutions to problems, and the various topics lectured.

M4038 - ECTS

This course is intended for students to acquire basic knowledge of the theory and numerical treatment of partial differential equations.

M4082 - ECTS To become familiar with the research and selection of  scientific and technical references, to analyse and discuss scientific papers and to acquire mathematical communication skills, both oral and written.

CC4019 - ECTS

The aim of of information theory is to expose fundamental concepts related to information and its applications in systems and communications networks and computer science.

M4059 - ECTS To introduce the fundamental concepts and principles of risk theory.
To provide a fundamental knowledge of the commonly used stochastic models and techniques in non-life insurance mathematics.

M4031 - ECTS

The course presents the main concepts and techniques of digital image processing and analysis. The main goal is that in the end of the course the students will be able to plan and implement algorithms for information extraction from images.

The course orientation focus on the understanding of concepts and methods, and its effective use in synthetic and experimental data analysis. The course makes an extensive use of advance computational tools (MATLAB).

CC4024 - ECTS Identification and application of data mining techniques to extract knowledge from different data sources (e.g. text, web).

M4063 - ECTS

Introduce the main concepts and methods of supervised and unsupervised classification.

M4015 - ECTS To provide the students with advanced regression techniques for Gaussian, binomial and Poisson regressions, especially designed for repeated measures and longitudinal data.

M4049 - ECTS

The main objective of the course is to introduce in a rigorous way the fundamentals of Game Theory, with particular emphasis on Nash equilibria.

CC4020 - ECTS To improve background on techniques for designing algorithms and analysing their correctness and complexity.
To know and apply methods for finding exact and approximate solutions for hard problems.

M4026 - ECTS

The course will focus on Markov decision processes and some generalizations. Markov decision processes, also referred to as stochastic dynamic programs or stochastic control problems, are models for sequential decision making when outcomes are uncertain. The Markov decision process model consists of decision epochs, states, actions, rewards, and transition probabilities. Choosing an action in a state generates a reward and determines the state at the next decision epoch through a transition probability function. Policies or strategies are prescriptions of which action to choose under any eventuality at every future decision epoch. Decision makers seek policies which are optimal in some sense. An analysis of this model includes

1. providing conditions under which there exist easily implementable optimal policies;
2. determining how to recognize these policies
3. developing and enhancing algorithms for computing them; and
4. establishing convergence of these algorithms.