Supervised Learning: Classification and Regression

This lecture covers the fundamentals of supervised learning, focusing on classification and regression. Topics include logical inferences, induction, supervised learning models, representation of problems, model types, decision boundaries, and overfitting. The instructor discusses the Perceptron algorithm, Support Vector Machines, attribute selection, model logic, specialization, generalization, and the concept of disjunctive models. The lecture also delves into numerical attributes, linear regression, regularization, optimization, and the logistic transformation. Additionally, it explores the concept of kernels, non-linear transformations, and the logistic function. Practical applications such as spam filtering using logistic regression are also discussed.

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