This course provides a foundational introduction to machine learning, covering both supervised and unsupervised approaches. It begins with theoretical concepts and practical tools for exploratory data analysis, addressing different data types and formats and guiding students from raw data through to result validation. The main steps of a general machine learning pipeline are then introduced, followed by a discussion of key models, model evaluation, and hyperparameter tuning.
Along the way, unsupervised techniques such as dimensionality reduction are also presented.