Core Concepts in Machine Learning 1

This lecture includes a description of machine learning nomenclature (supervised, unsupervised), the basics of "learning", bias-variance tradeoff and model complexity, model selection and cross-validation, and model evaluation metrics.

Core Concepts in Machine Learning 1

This lecture includes a description of model evaluation and selection, cross-validation and pitfalls, regularization, selecting the ridge hyperparameter, dimensionality reduction, cross-validating feature selection, and linear decompositions.

YouTube Lecture | Slides

YouTube Lecture | Slides

Linear models in scikit learn

Cross-validation in scikit learn

In-depth lecture on supervised learning with linear regression (time-stamped video at 40mins)

James et al.: Chapter 2.1 of 'An Introduction to Statistical Learning'

James et al.: Chapter 2.2 of 'An Introduction to Statistical Learning'

AI/ML Week 1 Data Exercise

AI/ML Week 1 Solutions