1. Concepts on Emerging and Intelligent Technologies;
2. Emerging Technologies;
3. Intelligent Ecosystems;
4. Industry 4.0 and 5.0;
5. Internet of Unmanned Vehicles;
6. Smart Solutions: applications, architectures and technologies;
7. Development of intelligent solutions supported by emerging technologies.

1. Introduction to machine learning
2. Neural networks
   2.1 Structure of an artificial neuron
   2.2. The Perceptron
   2.3 Multi-layer perceptron
   2.4 Activation functions
   2.5 Stochastic gradient
   2.6 Backpropagation algorithm
   2.7 Loss functions
   2.8 Validation and evaluation of models
   2.9 Regularisation and optimisation
3. Convolutional neural networks
   3.1 Convolutional layers
   3.2 Pooling layers
   3.3 Dense layers
   3.4 Training a convolutional neural network
   3.5. Transfer learning
   3.6. Applications in image processing
4. Recurrent neural networks
5. Attention mechanism
6. Graph neural networks
7. Reinforcement learning

1. introduction to Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality
2. User interaction based on VR and AR
3. Using VR and AR in digital twins
4. Development of VR and AR solutions
5. Evaluating VR and AR solutions

1. Introduction to Mobile Computing Platforms
   1.1. Contextualisation, trends and prospects
   1.2. Platforms and operating systems (iOS / Android)
   1.3. Native development vs cross-platform development
2. Fundamental concepts of native development
   2.1. Programming languages (Swift / Kotlin) and development paradigms
   2.2. Development environments (Xcode / Android Studio)
   2.3. Development approaches (Storyboard-based, SwiftUI / View-based, Compose)
   2.4. Interfaces and navigation
   2.5. Anatomy, life cycle and architecture of mobile applications
   2.6. Persistence and Networking
3. Introduction to Cross-Platform Development
4. Verification and validation of mobile applications

Students must choose one of the following curricular units:

Design and Development of Digital Games

1. Game Design
2. Games User Experience
3. Introduction to a contemporary game engine
4. 2D and 3D development
   4.1. Simulação de Física
   4.2. Animação
   4.3. UI e Menus
   4.4. Áudio
5. Game Programming Patterns

Management of Informatic Projects

1. A framework for Project Management
2. Project Lifecycle
3. Standardization of Project Management
4. Develop the Project Charter
5. Requirements and Defining the Scope of the Project
6. Quality Planning Project
7. Plan Shares the Risk Management
8. Planning the Communications for the Project
9. Planning the Procurement Project
10. Create the Project Plan
11. Recruiting, Managing and Developing the Team
12. Controlling Changes
13. Managing the Procurement of Project
14. Closing the Project

1. Introduction to cloud computing
   1.1 Paradigm and conceptual model
   1.2 Basic concepts and terminology
   1.3 Goals and benefits, risk and challenges
   1.4 Usage scenarios
2. Concepts and models
   2.1 Cloud computing characteristics
   2.2 Delivery models
   2.3 Deployment models
   2.4 Service-Level Agreement
3. Performance, scaling and load balancing
   3.1 Performance and scale
   3.2 Vertical scaling
   3.3 Horizontal scaling
   3.4 Load balancing
   3.5 Performance tests and analysis
4. Security and disaster recovery
   4.1 Security models applied to cloud computing
   4.2 Techniques and procedures for disaster recovery applied to cloud computing
5. Tools, procedures and economic models
   5.1 Developer point-of-view
   5.2 Enterprise point-of-view
   5.3 Startup point-of-view
6. Orchestration of cloud services
   6.1 Planning and defining a cloud orchestration architecture
   6.2 Implementing and monitoring a cloud orchestration

1. Types of Artificial Intelligence
2. Deep Learning in Natural Language Processing
   2.1 Text data preparation
   2.2 Representation of groups of words
   2.3 Transformer architecture
   2.4 Sequence-to-sequence learning
3. Generative deep learning
   3.1 Text generation
   3.2 DeepDream image modification
   3.3 Neuronal style transfer
   3.4 Image generation with variational autoencoders
   3.5 Generative adversarial networks (GAN)
4. Explainability models
5. Good practices
   5.1 State of the art of deep learning models
   5.2 Model training optimization

1. Security Fundamentals
2. Challenges imposed by mobility, IoT and cloud computing
3. Principles of network security
4. Network threats and attacks
5. Protecting the network
6. Security monitoring
7. Intrusion data analysis
8. Incident response and handling

Students must choose one of the following curricular units:

Enterprise Architectures

1. Enterprise Architectures
   1.1 Introduction to EA
   1.2 Frameworks and Tools
   1.3 ArchiMate
   1.4 TOGAF
2. Service-Oriented Architectures (SOA)
   2.1 SOA Principles and strategy
   2.2 SOA patterns
3. Enterprise IS foundations
   3.1 ESB
   3.2 API management & (micro) Service Layers
   3.3 Identity Services
   3.4 RPA
4. Technologically Innovative Business Models
   4.1 Busines Analysis Overview
   4.2 Digital Business Case studies
   4.2.1 Artificial Intelligence
   4.2.2 Augmented Reality
   4.2.3 Natural User Interfaces
   4.2.4 Analytics & Visualization
   4.2.5 Blockchain
   4.2.6 E-commerce & SAAS
   4.2.7 Crowdfunding
   4.2.8 Robotics

Software Quality

1. Software Testing
   1.1. Fundamentals of Testing
   1.2. Testing Throughout the Software Lifecycle
   1.3. Static Techniques
   1.4. Test Design Techniques
   1.5. Test Management & Tool Support for Testing
2. The Deployment Pipeline that supports Continuous Integration
   2.1. Foundations of Software Delivery
   2.2. Defining a Deployment Pipeline
   2.3. The Commit Stage
   2.4. Automated Acceptance Testing
   2.5. Deploying and Releasing Applications
3. Quality Attributes in Software Architecture
   3.1. Understanding Quality Attributes
   3.2. Architecture and Quality
   3.3. Containerisation

Free Elective

The “Free Elective” unit must be selected from among the curricular units available for this purpose, under the guidance of the higher education institution.

Internet of Things: concept, communication, architectures and interoperability.

Smart Objects and Ambient Intelligence: context, interface and user activities.

Learning models for adaptive and evolving environments.

Ambient Intelligence Project.

Project

Students will develop an original work in the Computer Engineering – Mobile Computing scientific areas. The work plan shall be approved by the school scientific body and shall be mostly done in academic and research environment.

Internship

Students will develop an original work in the Course’s scientific areas. The work plan shall be approved by the school scientific board and shall be mostly done in a professional environment.

Dissertation

Students will develop an original work in the Computer Engineering – Mobile Computing scientific areas. The work plan shall be approved by the school scientific body and shall be mostly done in academic and research environment.