Ingredients of Machine Learning

27 Setembro 2016, 13:00 • João Marques Silva

The second lecture provided an overview of essential concepts in machine learning and data mining, including features, tasks and models, supervised/unsupervised learning, predictive and descriptive models, grouping vs. grading. The lecture described common tasks, including classification, regression, clustering and association rule discovery. The lecture also briefly introduced a categorization of models in machine learning, including geometric, probabilistic and logical, and listed models studied in the course: trees, rules, naive Bayes, kNN, linear models, SVMs, Kmeans, GMMs, and association rules. The lecture concluded by formalizing (binary) classification.