The goal of these lecture notes is to review the problem of free energy minimization as a unified framework underlying the definition of maximum entropy modelling, generalized Bayesian inference, learning with latent variables, statistical learning analysis of generalization,and local optimization. Free energy minimization is first introduced, here and historically, as a thermodynamic principle. Then, it is described mathematically in the context of Fenchel duality. Finally, the mentioned applications to modelling, inference, learning, and optimization are covered starting from basic principles.

翻译：这些演讲说明的目的是审查将免费能源最少化的问题,将其作为一个统一框架,其基础是界定最大通则建模、普遍贝叶斯推论、学习潜在变量、对一般化进行统计学习分析以及地方优化。 免费能源最少化首先作为热力学原则在这里和历史上引入。 然后,从数学角度将其描述为Fenchel的二元性。 最后,所提到的建模、推论、学习和优化应用从基本原则开始就包含在内。