Thesist

Recommended Prerequisites

BSc in Artificial Science and Data Science or similar and courses of the 1st year of the Master in Artificial Inteligence and Data Science.

Teaching Methods

Development in a company or research laboratory under the supervision of the institution advisor and by an advisor from UC for the internships in companies. The evaluation is based on the proposal document and a presentation by the student, by a jury nominated by the Scientific Commission that includes the president, non-supervisor jury member, and the supervisors, according to the rules of this course. With The student will receive comments / recommendations that must be taken into consideration in the second semester.

Learning Outcomes

The main goals of this course are the following:
- Scientific research methodology for data science
- Initiation of basic and applied research activities
- Analysis of the state of the art
- Justified choice of tools and methodologies to use
- Elaboration of hypotheses
- Completion of research work and analysis of results
- Communication of scientific results achieved

Work Placement(s)

No

Syllabus

All the contents in the area of Artificial Inteligence and Data Science and Engineering, with emphasis on research methodologies.

Head Lecturer(s)

Alberto Jorge Lebre Cardoso

Assessment Methods

Assessment
Project: 100.0%

Bibliography

Dependente de cada dissertação/Depends on the type and nature of the Thesis.