Machine Learning: Features and Model Selection

This lecture covers the importance of features in machine learning, including different types and the process of feature engineering. It also discusses the evolution of machine learning models before and after 2012, emphasizing the impact of deep learning. The lecture further explores the challenges of labeling data and the use of crowdsourcing for data annotation. Model selection techniques, such as hyperparameter tuning and evaluation metrics like precision, recall, and F1-score, are also explained. The lecture concludes with a focus on bias-variance tradeoff, the significance of data in improving algorithms, and the assessment of classification models through error metrics and ROC analysis.