Description

As the scale and scope of data collection continue to increase across virtually all fields, machine learning has become a critical toolkit for anyone who wishes to extract important patterns and trends, and understand “what the data says”. Traditional linear statistical models are often hampered by their linear assumptions which rarely hold in real data analysis.

In this project the students will learn how to construct nonlinear machine (statistical) learning models for real world data. There are various machine learning models in the literature including for example random forest, boosting and neural networks. Each student can choose a real data set of their interest and focus on one or two models. The aim of this project is to train the students to have the ability to identify and apply appropriate machine learning methods to real-world problems.

Prerequisites

Resources

An Introduction to Statistical Learning https://www.statlearning.com/

Probabilistic Machine Learning: An Introduction https://probml.github.io/pml-book/book1.html