Lecture notes from the course given by Professor Sara A. Solla at the Les Houches summer school on "Statistical physics of Machine Learning". The notes discuss neural information processing through the lens of Statistical Physics. Contents include Bayesian inference and its connection to a Gibbs description of learning and generalization, Generalized Linear Models as a controlled alternative to backpropagation through time, and linear and non-linear techniques for dimensionality reduction.

翻译：本讲义源自萨拉·A·索拉教授在莱苏什暑期学校“机器学习的统计物理学”课程中的授课内容。该讲义从统计物理学视角探讨神经信息处理问题，内容涵盖：贝叶斯推理及其与吉布斯描述中学习与泛化过程的关联，作为时间反向传播可控替代方案的广义线性模型，以及用于降维的线性与非线性技术。