Week #3 - Supervised Machine Learning

Learning objectives:

• Formalize the supervised machine learning setup, including understanding the concepts of training data, feature spaces, label spaces, and hypothesis functions.
• Comprehend different types of classification problems (binary, multi-class) and regression, along with their corresponding label spaces and feature vector characteristics.
• Understand the process of learning a hypothesis function, including selecting an appropriate algorithm and finding the best function within the hypothesis class using loss functions.
• Grasp the concept of generalization and the importance of train/test splits to address overfitting concerns, including the role of validation data in model development.
• Learn how to properly train and evaluate a classifier, including the formulas for minimizing training loss and calculating testing loss, as well as understanding how these relate to the true generalization loss.

Laboratory

https://classroom.github.com/a/AUoF7uBb