Principles of Artificial Intelligence and Data Science

Code: INF14386L

6 ECTS

Duration: 15 weeks/156 hours

Scientific Area: Informatics

Teaching languages: Portuguese

Languages of tutoring support: Portuguese, English

Regime de Frequência: Presencial

Sustainable Development Goals

Erradicar a pobreza - Acabar com a pobreza em todas as suas formas, em todos os lugares

Educação de qualidade - Assegurar a educação inclusiva, e equitativa e de qualidade, e promover oportunidades de aprendizagem ao longo da vida para todos.

Igualdade de género - Alcançar a igualdade de gênero e empoderar todas as mulheres e meninas.

Reduzir as desigualdades - Reduzir as desigualdades dentro dos países e entre eles.

Learning Goals

This course offers a comprehensive overview of fundamental topics in Data Science and Artificial Intelligence, both from a mathematical and computational perspective. Special emphasis is given to the basic classes of techniques and methods, the theoretical foundations of Data Science and Artificial Intelligence. In this way, it provides an overview of several topics that will be covered in much more detail throughout the degree.

Introduction to Data Science
- Implementation of Data Science solutions, basic data analysis and visualization.
- Introduction of the Data Science process, and appropriate methodology.
- Examples of core Data Science methods with case studies such as in clustering, classification and regression.

Artificial Intelligence
- Introduction to classical AI and Machine Learning, including the relationship tor areas such as algorithms and optimization, and AI philosophy.
- Examples of methods and applications of AI, in classical AI (search and constraint satisfaction), and ML-based (search engines, naive Bayes and neural networks)

Teaching Methods

Teaching methodologies:

Theoretical classes; laboratory practical classes with problems that accompany the theoretical material.

Provision of exercises, of gradual difficulty, covering the topics taught, for students to practice mastery of the subject.

Evaluation:

Assessment

Continuous assessment - consisting of 2 components:
* two frequencies (40% each)
* project (20%)

Final assessment - consisting of 2 components:
* exam (80% each)
* project (20%)

The final grade is obtained through the weighted average of the 2 components. The student is approved if the final grade is equal to or greater than 10.