Machine Learning Crash Course 2015

Dive into this comprehensive machine learning crash course that covers fundamental concepts and practical applications across nine detailed sessions. Master local methods and model selection techniques, explore regularization networks including linear models and kernel methods, and understand dimensionality reduction through Principal Component Analysis (PCA). Learn variable selection and sparsity methods, clustering algorithms, and discover real-world applications of machine learning. Conclude with hands-on experience using GURLS, a user-friendly machine learning library designed to make implementation accessible. Each session builds upon previous concepts, providing a structured pathway from theoretical foundations to practical implementation in just over 10 hours of instruction.

Syllabus

Introduction to Machine Learning - Lorenzo Rosasco
Local Methods and Model Selection - Lorenzo Rosasco
Regularization Networks I Linear Models Francesca Odone
Regularization Networks II Kernels Francesca Odone
Dimensionality Reduction and PCA Lorenzo Rosasco
Variable Selection and Sparsity Lorenzo Rosasco
Clustering Francesca Odone
Applications of Machine Learning Francesca Odone
GURLS Machine Learning made Easy Alessandro Rudi