Introduction to Machine Learning

[0.50] credits · Undergraduate

This course will cover the fundamentals of data science and machine learning, focusing on model development, evaluation, and deployment. Both supervised and unsupervised learning will be introduced. Students will learn the key steps in building and deploying machine learning models and will develop an understanding of the issues involved with overfitting and underfitting. Classification and regression methods will be studied along with their evaluation metrics. Other topics will include machine learning algorithms including ensemble methods, support vector machines, the kernel trick and k-nearest neighbours as well as cross-validation and hyperparameter tuning.