This paper summarizes my lecture at the Machine Learning Summer School 2003, Tübingen.
Abstract: This contribution presents an overview of the theoretical and practical aspects of the broad family of learning algorithms based on Stochastic Gradient Descent, including Perceptrons, Adalines, K-Means, LVQ, Multi-Layer Networks, and Graph Transformer Networks.
@incollection{bottou-mlss-2004, author = {Bottou, L\'{e}on}, title = {Stochastic Learning}, booktitle = {Advanced Lectures on Machine Learning}, pages = {146-168}, publisher = {Springer Verlag}, year = {2004}, editor = {Bousquet, Olivier and von Luxburg, Ulrike}, series = {Lecture Notes in Artificial Intelligence, LNAI~3176}, address = {Berlin}, url = {http://leon.bottou.org/papers/bottou-mlss-2004}, }