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Learning outcomes |
At the end of the course, students should be able to:
* Understand concepts associated with Data Science, namely the distinction between data, information and knowledge, different storage formats, and the application of data cleaning and normalisation techniques;
* Analyse and interpret data using Data Science methods, namely collection and integration techniques, exploratory analysis and statistical and visualisation methods, with a view to knowledge extraction;
* Know and apply the main IA learning paradigms, namely supervised, unsupervised and reinforcement learning, understanding their theoretical foundations and scope of application;
* Know and explore artificial intelligence techniques in the areas of search, generative AI and computer vision, understanding the theoretical foundations and potential application of each approach.
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Syllabus |
1. Fundamentos de Dados: dados e tipos, informação vs conhecimento, viés na recolha, formatos CSV/JSON/XML/Parquet.
2. Recolha e Ingestão: APIs, bases de dados, web scraping, ETL/ELT, integração, data cleaning (missing values, outliers, inconsistências, escalas).
3. Análise e Preparação: análise exploratória, estatísticas, visualização, normalização (máx., Min-Max, Z-score, IQR).
4. Análise Preditiva: introdução, métodos, métricas.
5. Algoritmos de Pesquisa: DFS, BFS, gulosa, A*, MinMax, Alpha-Beta, profundidade.
6. Aprendizagem Supervisionada: função de custo, gradiente, redes neuronais.
7. Aprendizagem Automática: generalização, protocolos, validação cruzada.
8. Aprendizagem Não Supervisionada: clustering, K-means, agglomerative.
9. Aprendizagem por Reforço: Q-Learning.
10. IA Generativa: BoW, TF-IDF, embeddings, transformers, LLMs.
11. Visão Computacional: classificação, CNNs, VLMs, foundational 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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