MECD01 - ECTS

Students should have fundamental concepts in four key areas for MDSE: Statistics, Signal Processing, Databases and Programming.

Provide students with an integrated view of Statistics and of its usefulness, making them capacitated users of Descriptive Statistics and Statistical Inference.

Signal Processing module aims to provide students with concepts, techniques and tools of analysis and design in this field.

About Databases students should be able to describe and analyze the requirements of an IS, represent them using a UML class diagram and transform it into a relational model. Students should also be able to use the SQL language to create, manipulate and query databases.

Concerning Programming, students should acquire fundamental knowledge on procedural programming techniques and be able to develop programs, using the Python language.

MECD02 - ECTS

Background:
After a season in which different companies/institutions very invested in data collection by computerizing its operations (e.g. sensors, GPS systems), and in which many and varied new data sources have emerged (e.g. social networks), there is now the need to place such data at the service of those companies. The goal is to be able to extract knowledge from these data in order to improve efficiency and gain competitive advantage. From this need arises the Curricular Unit  (UC) of Introduction to Machine Learning and Knowledge Extraction.

Objectives:
The student should be able to: (1) Use adequately descriptive statistics for data description; (2) To describe the different stages of the process of knowledge discovery CRISP; (3) to use and analyze the results of some of the main methods of classification and regression; (4) to use and interpret cluster analysis methods; (5) to use and interpret methods of association rules; (6) To be able to develop a project on Machine Learning or Knowledge Discovery using the CRISP-DM methodology.

MECD03 - ECTS

The objective is to introduce the students to two major tools for the exact resolution of optimization problems:

• Mathematical programming – suitable to problems with continuous and combinatorial decisions;
• Constraint programming – suitable to combinatorial problems, especially when the solution space is small.

MECD04 - ECTS

This curricular unit has two main objectives, prepare students to:

(i) use the principles and techniques for pre-processing and prepare a data in order to obtain a dataset with quality to be analyzed, in order to obtain results with quality

(ii) and to use methods for visual representation of data that improves understanding and gain new insights on complex data.

MECD05 - ECTS

The students should be able to design, build and explore data warehouses.

MECD07 - ECTS Extracting information from large sets of data -- known as “big data” –has been the driver for several large and small companies in the last years and has imposed a specific set of challenges, that this course addresses. The goal of this curricular unit is to familiarize the student with the major paradigms, challenges, and approaches at developing big data applications and systems.

MECD06 - ECTS

This course provides a set of subjects (topics) that are the core of the Artificial Intelligence and Intelligent System area.

The main objectives are:

• To know what characterizes and distinguishes AI and how to apply it.
• To know how to automatically represent, acquire, manipulate and apply knowledge using Computational Algorithms and Systems.
• To develop small projects using AI techniques.

Percentual Distribution: Scientific component: 50%; Technological component: 50%

MECD08 - ECTS

The objective is to consolidate the skills acquired in the 1st year of the master's degree. This objective will be achieved through the development, in groups, of a project, within the scope of a real organization, and developed in contact with employees of that organization.

MECD11 - ECTS

The general aim of the course is to create skills in the treatment of complex data. The goal is to develop the ability to process data that are not simply table of i.i.d. observations. The types of complex data (CD) covered include those that are important today (graphs, and spatio-temporal data). However, the course will be flexible to accommodate new types or sources of data. Students will be prepared to develop techniques for new types of data they might be confronted with in their professional lives.

MECD17 - ECTS Nesta UC pretende-se dotar os alunos com as ferramentas essenciais à prospecção, refinamento, avaliação e implementação de novos negócios (incluindo negócios de base tecnológica).

MECD12 - ECTS

Students, in the context of a dissertation topic, and in close collaboration with a designated advisor, will review the literature and survey the state of the art on the problem(s) to be addressed, and establish a work plan with the project tasks to be developed later in the Dissertation course. The research objectives/questions, methodology, techniques, and possible tools to use should be clearly identified.

MECD16 - ECTS To give students a broad, but simultaneously in-depth, overview of search and optimization methodologies, applicable to the resolution of multi-disciplinary decision problems, under a decision support framework.

MECD10 - ECTS Ability to work in team, organization and planning.
Ability to analyze and synthesize knowledge.
Ability to develop machine learning systems depending on existing needs and apply the most appropriate technological tools. Know, apply and evaluate advanced learning models.
Know deep learning techniques, with end-to-end training approaches, and minimization of the use of tagged data. Solve applications using advanced auto-learning methods. Acquire the learning skills that allow to continue studying in a way that will be largely self-directed or autonomous.

MECD14 - ECTS The main goal of the course is to study and acquire skills concerning the use of "Domain Background Knowledge" (DBK) used in complex problem solving. The used of such DBK can be in the development of Knowledge Based Systems (like Expert Systems), or to be used by propositional Machine Learning algorithms, by means of data sets enrichment, or, directly, in multi-relational algorithms.

MECD15 - ECTS This curricular unit aims to provide students with the necessary tools and methodologies in the design, simulation, and analysis of intelligent systems.

MECD09 - ECTS

Computer vision focuses on extracting "useful information" from images and videos. Examples of "useful information" include, for example, detection and identification of human faces and gestures, and tracking moving people or vehicles in a video sequence. Computer vision algorithms have found a wide range of applications in the industrial, military and medical fields. This course is an introduction to basic concepts and methods in computer vision. Upon completion of this course, students will:

-understand and be able to explain the basic concepts of computer vision and the fundamental algorithms for manipulation of images and video sequences;

-have knowledge of existing methods for visual data analysis and be able to apply them in practical situations;

-acquire skills to use a library, like OpenCV, that implements some of the analyzed algorithms, and to implement novel algorithms described in the literature;

-be able to analyze and understand selected scientific papers in computer vision.

MECD13 - ECTS