Trees, SVM and Unsupervised Learning

The module provides an introductory overview of the course and introduces the course instructor.

To begin the course, we will learn about support vector machines (SVMs). SVMs have become a popular method in the field of statistical learning due to their ability to handle non-linear and high-dimensional data. SVMs seek to maximize the margin, or distance between the decision boundary and the closest data points, to improve generalization performance.

Neural Networks have become increasingly popular in the field of statistical learning due to their ability to model complex relationships in data. In this module, we will cover introductory concepts of neural networks, such as activation functions and backpropagation.

Welcome to the final module for the course. This module will focus on the ensemble methods decision trees, bagging, and random forests, which combine multiple models to improve prediction accuracy and reduce overfitting.