| Code |
16668
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| Year |
1
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| Semester |
S2
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| ECTS Credits |
6
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| Workload |
PL(30H)/T(30H)
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| Scientific area |
Informatics
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Entry requirements |
N/A
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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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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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Teaching Methodologies and Assessment Criteria |
Teaching/Learning Assessment
• T – Written Test: 70% (individual assessment of theoretical and practical knowledge)
• TP – Group Practical Project: 30% (development of an applied Data Science project with a final report)
The Teaching/Learning Classification (CEA) is calculated as follows:
CEA = 0.7T + 0.3TP
The student passes the course unit and is exempt from the final exam if a final grade equal to or higher than 9.5 (out of 20) is obtained.
Assessment by Final Exam
• E – Written Test: 70% (individual written examination)
The final grade in the exam period takes into account the practical project grade already obtained during the Teaching/Learning phase and is calculated as follows:
Final Grade = 0.7E + 0.3TP
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Language |
Portuguese. Tutorial support is available in English.
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