Inteligência Artificial

To successfully engage with this module, students should have:

• Basic understanding of programming concepts (variables, loops, functions)
• Experience with at least one programming language
• Basic problem-solving and algorithmic thinking skills

The course combines theoretical and practical approaches to ensure that students acquire both conceptual knowledge and the technical skills in the artificial intelligence field.

1. Expository Teaching and Demonstration
Each theory-based lesson begins with a structured presentation of fundamental concepts.
Live coding demonstrations are conducted to showcase best programming practices and to explain abstract concepts in a practical manner.

2. Active Learning and Problem-Solving (Problem-Based Learning – PBL)
Students solve practical exercises during lessons to reinforce their knowledge.
Problem-solving focuses on:

• Applying best development practices.
• Debugging code and identifying errors.
• Optimising solutions.

3. Collaborative Learning
Working in pairs or small groups to discuss and solve challenges.
Code reviews among students to encourage critical analysis and feedback exchange.
Guided discussions to foster critical thinking about architecture and best practices.

The Artificial Intelligence course unit aims to equip students with fundamental knowledge and skills to understand, develop and apply AI systems in industrial contexts.

At the end of the course unit, students should be able to:

• Understand the theoretical foundations of artificial intelligence and its main approaches
• Master search algorithms and knowledge representation in intelligent systems
• Apply supervised and unsupervised machine learning techniques
• Develop AI solutions using specialized libraries
• Understand and apply ethical principles in the development and use of AI systems

1. Foundations of Artificial Intelligence
2. Search Algorithms and Problem Solving
3. Knowledge Representation and Expert Systems
4. Machine Learning Fundamentals
5. Advanced Machine Learning Techniques
6. AI in Industrial Applications
7. Ethics and Responsibility in AI

Theoretical-Practical Test (50%)
Individual assessment on fundamental AI concepts, search algorithms, machine learning and industrial applications. Includes practical implementation exercises and critical analysis.

Requires obtaining a minimum of 35%.

Final Project (30%)
Development of an AI application in industrial context, carried out in groups or individually, with technical report and oral presentation. The project must include:
- Analysis of the problem and justification of the chosen AI approach
- Implementation using appropriate libraries
- Evaluation of results and discussion of ethical aspects
This grade will be valid for all examination periods.

Participation and Practical Exercises (20%)
Attendance, involvement in practical classes, submission of 3 exercises launched during the semester and participation in discussions about AI ethics.
In different examination periods, students may choose to waive this component and take the Theoretical-Practical Test for 70%.

Important Notes:
The Professor reserves the right to complement the assessment with an oral examination, of which students will be notified at least 48 hours in advance. Grades equal to or higher than 18.0 points mandatorily require oral defense.

NAO