Machine Learning-Based Predictive Modelling

This course will give you an overview of various machine learning-based approaches for predictive modelling, including tree-based techniques, support vector machines, and neural networks.  Starting with a range of decision trees, including CART and C4.5, for classification and regression, you will be introduced to the concepts of information gain and other variable analysis measures, as well as the structure of decision trees.  You will also learn various sampling techniques such as bagging and boosting, which improve robustness and overall predictive power, as well as random forests. The differences will be illustrated in a small case study.  The course also covers support vector machines by introducing you to the concept of optimising the separation between classes. You will then dive into support vector regression.   Finally, you will look at neural networks; their topology, the concepts of weights, biases, and kernels, and optimisation techniques. These provide a good basis for future deep learning efforts.   Various trade-offs in terms of understandability and predictive power are covered against the backdrop of the various training issues and solutions that make neural networks such a prevalent, but often challenging, technique.  In the final week of the course, you will focus on an in-depth analysis and comparison of the techniques in the context of various case studies.

Created by: The University of Edinburgh

Level: Advanced

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