Embark on a comprehensive journey through the fundamentals of machine learning in this 11-hour course. Begin with the basics of linear regression and progress to advanced topics such as multiple linear regression and singular value decomposition. Explore key concepts including likelihood, bias, and variance, while learning about regularization techniques and cross-validation. Dive into logistic regression and linear discriminant analysis before delving into neural networks and deep learning. Discover ensemble methods like boosting, bagging, and random forests, and explore unsupervised learning techniques such as clustering and expectation-maximization. Conclude with an in-depth look at dimensionality reduction methods, including principal component analysis, manifold learning, and t-SNE, providing a solid foundation for understanding and applying machine learning algorithms.
Overview
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.
Taught by
Tübingen Machine Learning
