Introduction to Machine Learning

This course introduces fundamental machine learning methods, covering regression, classification, neural networks, ensemble methods, clustering, and dimensionality reduction. It includes topics such as regularization, cross-validation, expectation-maximization, PCA, manifold learning, and t-SNE.

Syllabus

Introduction to Machine Learning - 01 - Baby steps towards linear regression.
Introduction to Machine Learning - 02 - Multiple linear regression and SVD.
Introduction to Machine Learning - 03 - Likelihood, bias, and variance.
Introduction to Machine Learning - 04 - Regularization and cross-validation.
Introduction to Machine Learning - 05 - Logistic regression.
Introduction to Machine Learning - 06 - Linear discriminant analysis.
Introduction to Machine Learning - 07 - Neural networks and deep learning.
Introduction to Machine Learning - 08 - Boosting, bagging, and random forests.
Introduction to Machine Learning - 09 - Clustering and expectation-maximization.
Introduction to Machine Learning - 10 - Principal component analysis.
Introduction to Machine Learning - 11 - Manifold learning and t-SNE.