This course covers the fundamentals of machine learning, including linear regression, logistic regression, neural networks, clustering, and dimensionality reduction techniques like PCA and t-SNE. Students will learn about regularization, bias-variance tradeoff, and ensemble methods such as boosting and bagging. The teaching method involves lectures and practical examples. This course is designed for individuals interested in gaining a foundational understanding of machine learning concepts and techniques.
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
