DSCI 571: Supervised Machine Learning I

Welcome to DSCI 571, an introductory supervised machine learning course! In this course we will focus on basic machine learning concepts such as data splitting, cross-validation, generalization error, overfitting, the fundamental trade-off, the golden rule, and data preprocessing. You will also be exposed to common machine learning algorithms such as decision trees, K-nearest neighbours, SVMs, naive Bayes, and logistic regression using the scikit-learn framework.

Course Learning Outcomes

By the end of the course, students are expected to be able to:

• describe supervised learning and identify what kind of tasks it is suitable for;
• explain common machine learning concepts such as classification and regression, data splitting, overfitting, parameters and hyperparameters, and the golden rule;
• identify when and why to apply data pre-processing techniques such as imputation, scaling, and one-hot encoding;
• describe at a high level how common machine learning algorithms work, including decision trees, K-nearest neighbours, and naive Bayes;
• use Python and the scikit-learn package to responsibly develop end-to-end supervised machine learning pipelines on real– world datasets and to interpret your results carefully.

Also check out our Introduction to Machine Learning course developed for the Key Capabilities for Data Science program.