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Machine Learning and Data Mining
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[ms_accordion_item title=”Coordination” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]Pedro Pereira Rodrigues 
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[ms_accordion style=”simple” type=”1″ class=”” id=””] [ms_accordion_item title=”Intended learning outcomes of the curricular unit” color=”#7b89b6″ background_color=”” close_icon=”” open_icon=”” status=”open”]
This unit aims to empower students with the necessary knowledge and skills to:
• interpret and apply machine learning techniques in health databases
• identify problems that can be addressed with data mining processes
• recognize the most common tasks of knowledge discovery (e.g. clustering, classification, association, regression)
• apply and interpret the obtained results, according to technical accuracy and impact in the domain.
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[ms_accordion style=”simple” type=”1″ class=”” id=””] [ms_accordion_item title=”Syllabus” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]
The Data Mining Process
• Business understanding
• Data understanding
• Data pre-processing
• Data modelling
• Evaluation of data mining models
• Deployment of mining results
Machine Learning
• Learning concepts from data
• Inductive vs deductive processes
• Inductive bias
• Validation of learned models
• Error measures and estimation processes
Supervised Learning
• Decision trees
• Bayesian networks
• Neural networks
• Deep learning and big data mining
Unsupervised Learning
• Cluster analysis
• Outlier and anomaly detection
• Association and frequent pattern analysis
• Making sense of big databases
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The syllabus includes all necessary and sufficient concepts to support the students’ knowledge and skills needed to properly understand, apply and integrate different machine learning methods in real-world medical problems. Students will be able to understand the problems, to apply data modeling methods, and interpret results in the context of practical medicine, data analysis, and research in health information systems.
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[ms_accordion_item title=”Teaching methodologies” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]
Theoretical lectures and practical lessons, with topic discussion, individual and group exercises, and hands-on training on medical scenarios, with proper software. Evaluation will be based on individual and group assignments, oral presentations and final exam.
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Theoretical lectures are the most efficient means for transmitting the basic and advanced topics related to the use of machine learning techniques in health data. Moreover, hands-on lessons jointly with individual and group assignments, will allow a solid consolidation of competences and behaviors needed for a proper inclusion of these methods in daily research and in the intelligent health data analysis.
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[ms_accordion_item title=”Main bibliography” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]
The Elements of Statistical Learning: Data Mining, Inference and Prediction. T. Hastie, R. Tibshirani, and J. Friedman. Springer Verlag, 2001.
Data Mining: Concepts and Techniques. Jiawei Han, Micheline Kamber and Jian Pei. Morgan Kaufmann, 3rd edition, 2011.
Extração de Conhecimento de Dados – Data Mining. Ana Carolina Lorena, Katti Faceli, Márcia Oliveira, André Ponce de Leon Carvalho, João Gama. Edições Silabo, 2012.
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