| Código |
16668
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| Ano |
1
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| Semestre |
S2
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| Créditos ECTS |
6
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| Carga Horária |
PL(30H)/T(30H)
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| Área Científica |
Informática
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Learning outcomes |
At the end of the course, students should be able to:
1. Understand the different phases of a Data Science project and the techniques most used in each of these phases;
2. Understand the development of the field of Artificial Intelligence and the key milestones that have increased its relevance and impact on current society;
3. Know the principles behind the domains of machine learning, intelligent search, and knowledge representation and reasoning;
4. Produce reports containing the main information extracted from a dataset through the combination of Data Science techniques with Artificial Intelligence methods.
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Syllabus |
A. Historical perspective on Artificial Intelligence (AI) and Data Science (DS).
B. Impact of AI and DS on society and scientific and technological development.
C. Case studies with selected applications.
D. Ethical implications, security, and privacy. Interpretability of models.
E. Introduction to Artificial Intelligence. Intelligent agents. State space search.
F. Introduction to Data Science. Data and knowledge representation. Feature extraction. Machine learning.
G. Design and development of a mini project with AI and DS components.
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Teaching Methodologies and Assessment Criteria |
Teaching methodologies:
• Theoretical classes;
• Practical-laboratory classes;
• Group projects;
• Tutoring to clarify doubts and accompany the student in the development of his project.
Assessment Methods and Criteria:
The theoretical and practical components are evaluated using two main elements:
- a written test (T) of knowledge assessment, with a weight of 60% in the final grade;
- a practical work (TP1) supported by a written report and an oral presentation, weighing 15% in the final grade.
- a practical work (TP2) supported by a written report and an oral presentation, weighing 25% in the final grade.
Teaching-Learning Classification (CEA) = 0.6T + 0.15TP1 + 0.25TP2
Admission to the final exam: CEA >= 6 values (UBI regulation).
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Main Bibliography |
[1] D.P. Kroese, Z.I. Botev, T. Taimre, R. Vaisman. "Data Science and Machine Learning: Mathematical and Statistical Methods." Chapman & Hall/CRC, 2019.
[2] Hui Lin, Ming Li, Practitioner’s Guide to Data Science, 2023.
[3] Joel Grus. Data Science from Scratch, 2nd Edition. O'Reilly Media, Inc., 2019.
[4] Andriy Burkov. The Hundred-Page Machine Learning Book, 2019.
[5] S. Russell and P. Norvig; Artificial Intelligence: A Modern Approach, Pearson, 2021.
[6] Richard E. Korf. Artificial intelligence search algorithms. Algorithms and theory of computation handbook: special topics and techniques (2nd ed.). Chapman & Hall/CRC, 2010.
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| Language |
Portuguese. Tutorial support is available in English.
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