| 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 unit, the student should be able to:
• Understand the development of the field of Artificial Intelligence and the key milestones that have increased its relevance and impact on current society;
• Know the principles behind the domains of machine learning, intelligent search, and knowledge representation and reasoning;
• Understand the different phases of a Data Science project and the techniques most used in each of these phases;
• 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 |
Intelligent Search Algorithms:
- Pathfinding problems;
- Games;
- Constraint satisfaction problems.
Knowledge Representation and Reasoning Methods:
- First-order logic;
- Automated reasoning;
Machine Learning Methods:
- Supervised and unsupervised learning methods;
- Reinforcement learning;
- Discriminative and generative methods.
Data Collection and Ingestion Techniques:
- Data collection from APIs, Web pages, Databases;
- File formats (XML, JSON, CSV, Parquet).
Data Preprocessing Techniques:
- Data cleaning techniques;
- Data transformation techniques;
- Data reduction techniques.
Visual Representations of Data:
- Univariate and multivariate data representations;
- Conditional, temporal, and multi-aggregate value graphs.
Predictive Analytics Techniques:
- Classification methods;
- Regression methods;
- Evaluation metrics for predictive analytics models.
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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 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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Language |
Portuguese. Tutorial support is available in English.
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