Syllabus:

General objectives:
– It is intended to provide students with the knowledge to enable them to understand and identify emerging techniques and methods to support software development and to develop the capacity to identify and instantiate technological infrastructures supporting the construction and operation of complex, modular, distributed, and developed software in a collaborative way in short and demanding circuits.

Specific objectives:
i) Understand the emerging changes and challenges associated with the software development lifecycle requirement;
ii) Understand trends/approaches associated with the development of business WEB platforms, and the models and/or approaches underlying them;
iii) Identify tools and mechanisms that support the stages of software construction and integration;
iv) Design and operationalize technological infrastructures that allow to accelerate the construction and delivery of functional software collaboratively.

CONTENTS

1. Software Development Life-Cycle
2. Risk Factors in Software Development phases
3. Domain-Driven Design
4. Virtualization techniques to support software development
5. Migrate monolithic applications to microservices
6. Development of modern web applications
7. Patterns for the development of microservice-based architectures

Syllabus:

1. Know and to identify applicability scenarios for different methodologies/models and techniques for data storage;
2. Identify all aspects for the development and implementation of a Business Intelligence system;
3. Know and apply modeling techniques and concepts for building data storage systems and explore their potential through advanced techniques for knowledge discovery;
4. Know and identify the main methods and techniques for identifying, extracting, processing and loading data;
5. Identify and apply data exploration and analysis techniques.

CONTENTS

1. Exploring Operational Databases
2. NoSQL Databases
2.1. Document-oriented database
2.2. Graph-oriented database
3. Business Intelligence Systems planning and implementation
3.1. Study of Data Warehousing System Architecture and Components
3.2. Study of methodologies and application for Data Warehouse implementation
3.3. Study of concepts and techniques applied to multidimensional data modeling
3.4. Hierarchy concept, its study, and implementation
4. Study and application of analytical data processing techniques
5. Big Data concept and emerging technologies

Syllabus:

1. To assess and manage distributed and cloud solutions;
2. To understand and develop applications using web services;
3. To understand and develop scalable and reliable applications for cloud computing;
4. To manage infrastructure of distributed and cloud computing
5. To analyse and discuss concrete case studies;
6. Apply the introduced concepts.

CONTENTS

1. Distributed computing paradigm:
1.1. Architectures and models of distributed computing;
1.2. Distributed objects and remote invocation;
1.3. Web Services;
1.4. Replication and Consistency.
2. Cloud computing paradigm:
2.1. Architectures and applications of cloud computing;
2.2. Infrastructures (grid computing, virtualization, edge computing, cycle-sharing);
2.3. Storage;
2.4. Resource management (scheduling, migration, scalability, reliability).

Syllabus:

The general objectives of this course are:
– Identify, understand and characterize Virtual Reality and Augmented Reality projects;
– Design, evaluate and develop applications in the field of Virtual Reality and Augmented Reality.

More specifically, after completing this course the student should be able to:
I) Know the existing challenges in the development of applications for Virtual Reality and Augmented Reality;
II) Structure and distinguish the different components of a functional application of Virtual Reality and Augmented Reality;
III) Understand the use of frameworks and software standards used in the implementation of Virtual Reality and Augmented Reality components;
IV) Integrate principles of Computer Vision and object tracking in Augmented Reality applications integration between real world images and virtual object images;
V) Recognize examples of applications for Virtual Reality and Augmented Reality projects.

CONTENTS

1 – Introduction to Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR)
2 – Architecture of VR and AR systems and main components used
3 – Study of an example of a VR and AR framework
3.1 – Characterization of an VR and AR framework
3.2 – Overlapping virtual and real resources
3.3 – Description of the processing phases of AR applications: recognition and tracking of recognized patterns / objects
4 – Computer vision and dedicated algorithms for object recognition and tracking in virtual and real environments
4.1 – Characterization of a computer vision system and its application in RA
4.2 – Introduction to image acquisition, processing and analysis techniques
4.3 – Application of deep learning models to detect objects / patterns
4.4 – Application of computer vision algorithms and frameworks in VR and AR applications
5 – Examples of VR and AR applications in Industry 4.0

Syllabus:

Study of the techniques, methodologies, tools and legal constrains related to the implementation of privacy enabled systems.

After concluding this Curricular Unit, the student should be able to:
1. Identify problems that have impact on data privacy.
2. Know and understand privacy enhancing technologies.
3. Know and understand data anonymization techniques.
4. Know and understand techniques for the evaluation of the privacy of a system.
5. Evaluate the level of privacy of a system.
6. Implement systems on a privacy-by-design and privacy-by-default policies.

CONTENTS

1. Introduction
2. Online anonymity
3. Privacy and mobile apps
4. Anonymous communications
5. Data anonymization (k-anonymity, l-diversity, t-closeness)
6. Encryption (homomorphic, proxy and searchable)
7. GDPR and Data privacy impact acessments
8. Blockchain (cryptocurrencies, smart contracts)

*English* The courses available in English are depending on the number of attending students

Syllabus:

The objectives of this curricular unit are:
– know the fundamental concepts of machine learning;
– know the main techniques/algorithms according to the current state of the art;
– identify the best technique/algorithm to apply in a given scenario;
– creation of pipelines for model development;
– integration of models in software development.

More specifically, after completing this curricular unit, the student should be able to:
1. Know the fundamentals of Machine Learning;
2. Know and understand the algorithms that are more used;
3. Identify and apply the best algorithm given a specific scenario;
4. Development of pipelines with integration of multiple models;
5. Development and integration of models in software applications.

CONTENTS

1. Machine Learning Concepts
2. Linear Algebra Review
3. Machine Learning Libraries/Packages
4. Supervised Learning
a. Linear Regression
b. Logistic Regression
c. Support Vector Machines
d. Deep Learning (Neural Network and Convolution Neural Networks)
e. Decision Tree and Random Forest
f. k-NN
5. Unsupervised Learning
a. Clustering
b. Dimensionality Reduction
6. Applying ML in large datasets
7. Case studies
8. Docker and ML