12 ECTS; 3º Ano, 1º Semestre, 80,0 PL + 28,0 TP + 25,0 OT , Cód. 814361.
Lecturer
- João Manuel Mourão Patrício (2)
- Sandra Maria Gonçalves Vilas Boas Jardim (1)(2)
- Fernando Sérgio Hortas Rodrigues (2)
- Renato Eduardo Silva Panda (2)
(1) Lead Professor
(2) Teaching Professor
Prerequisites
Knowledge of Linear Algebra, Integral/Differential Calculus, Programming (Python), Machine Learning.
Objectives
1. Understand Artificial Intelligence research and development methodologies
2. Document and develop practical work in Artificial Intelligence
3. Participate in the Artificial Intelligence event
Program
1. Research and development methodologies in Artificial Intelligence
2. Documentation tools: Latex, Office, OpenOffice
3. Software versioning system: git
4. Definition of work. Definition of the execution phases and practices to be carried out. Definition of documentation methodologies to be
followed
5. Templates for communicating work results
6. Design of the implementation plan and methodologies to be used in the practical work
7. Organizing an AI event
Evaluation Methodology
At all assessment periods:
(60%) practical work in AI.
(20%) Holding a seminar on the topic of practical work.
(20%) Participation in the organization of an AI event.
The final classification results from the weighted average of the classifications obtained in each of the defined evaluation components.
Passing the UC implies a final classification equal to or greater than 10 points.
Bibliography
- --, -. A definir consoante os projetos a desenvolver. (Vol. --). (pp. -----). --: --
- Kottwitz, S. (2021). LaTeX Beginner's Guide: Create visually appealing texts, articles, and books for business and science using LaTeX. (Vol. 1). (pp. 1---). UK: Packt Publishing
- Liberty , J. e Galloway, J. (2021). Git for Programmers: Master Git for effective implementation of version control for your programming projects. (Vol. 1). (pp. 1---). UK: Packt Publishing
Teaching Method
Theoretical-practical classes: expository for the transmission of content and collaborative preparation for holding an AI event, as well as a seminar. Laboratory classes: design and implementation of a practical AI project.
Software used in class
Overleaf (collaborative writing of scientific documents)
Google Colab (computational development)
