Lecture
This lecture introduces the concepts of input representation and feature engineering in machine learning. It covers the role of input, different types of features, handling missing values, and techniques like data normalization, feature expansion, and imputation. The instructor explains how to transform raw data into meaningful feature vectors, the importance of clever feature design, and methods to preprocess data for machine learning algorithms.