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Syllabus |
A. Data Fundamentals: data and types, information vs knowledge, bias in data collection, file formats (CSV, JSON, XML, Parquet).
B. Data Collection & Ingestion: APIs, databases, web scraping, ETL/ELT, integration, data cleaning (missing values, outliers, inconsistencies, scales).
C. Analysis & Preparation: exploratory analysis, statistics, visualization, normalization (max value, Min Max, Z score, IQR).
D. Predictive Analysis: introduction, methods, evaluation metrics.
E. Search Algorithms: DFS, BFS, greedy search, A*, MinMax, Alpha Beta pruning, depth limited search.
F. Supervised Learning: cost function, gradient descent, artificial neural networks.
G. Machine Learning Evaluation: generalization error, evaluation protocols, cross validation.
H. Unsupervised Learning: clustering, K means, agglomerative clustering.
I. Reinforcement Learning: Q Learning.
J. Generative AI: BoW, TF IDF, embeddings, transformers, LLMs.
K. Computer Vision: image classification, CNNs, vision language 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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