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Machine learning
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Machine learning involves analyzing big data for insights and predictions. It uses algorithms to find patterns, from descriptive to predictive analytics. Key differences between AI and ML include AI's focus on human-like behavior and interaction, while ML specializes in data analysis and automation. Data preparation includes handling incomplete or noisy data, transforming data, and reducing it. Exploratory data analysis covers single and multivariate methods like histograms, scatterplots, and correlation measures. Classification tasks involve supervised learning to predict outcomes based on past data, using models like decision trees and evaluating acc
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- Machine learning involves analyzing big data to derive insights and implement data-driven strategies through descriptive, predictive, and perspective analytics.
- Data preparation includes handling incomplete and noisy data, transforming data for uniformity, reducing dataset size, and discretizing numerical attributes.
- Exploratory data analysis covers single and multivariate analysis using graphical methods, measures of central tendency, dispersion, and location, along with correlation and contingency tables.
- Classification in machine learning focuses on supervised learning tasks like separating customer groups into loyal or churners, evaluating models through accuracy, confusion matrices, ROC curves, and avoiding overfitting and bias-variance trade-offs.
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MACHINE LEARNING · 1° semestre · 5 CFU · INFO-01/A
This course gives an overview of techniques and algorithms in machine learning and pattern recognition. It provides students with the basic ideas and intuition behind modern machine learning methods as well as a more detailed coverage of most techniques. The course fits into the overall program curriculum pursuing some…