Class Topics on Machine Learning and its Applications

The curricular unit Topics in Machine Learning and Its Applications introduces the main concepts and techniques of machine learning, with a focus on data preparation, supervised and unsupervised learning, and model evaluation. Fundamental methods are covered, along with strategies for handling real-world data. This course falls within the field of data science and intelligent systems and is relevant to the study programme as it equips students with essential skills for data analysis and the development of machine learning-based solutions, with broad applicability across scientific and professional domains.

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Linear and Logistic Regression Preparing data for training Data Collection Data Cleaning Data Transformation Normalization Encoding Temporal Variables Bining Imbalanced Data Oversampling (Random, SMOTE, Augmentation) Undersampling Supervised Learning K-nearest neighbours (KNN) Nearest Centroid Decisions Trees Random Forest Support Vector Machines Unsupervised Learning Kmeans PCA Evaluation methods 

Lecturing consists of theoretical and practical classes. The theoretical component is essentially expository, the theory being presented together with concrete examples. In the practical component, practical programming problems related to the theory taught are developed and solved.

Müller, A. C., & Guido, S. (2016). Introduction to machine learning with Python: A guide for data scientists. O'Reilly Media, Inc.¿¿¿ Ananthaswamy, A. (2024). Why machines learn: The elegant math behind modern AI. Dutton. Gallatin, K., & Albon, C. (2023). Machine learning with Python cookbook: Practical solutions from preprocessing to deep learning (2nd ed.). O'Reilly Media.Gallatin, K., & Albon, C. (2023). Machine learning with Python cookbook: Practical solutions from preprocessing to deep learning (2nd ed.). O'Reilly Media.