About the Book
Table of Contents:
Natural computation: introduction; the brain; computational theory; elements of natural computation; overview; the grand challenge; notes; exercises. Part 1 Core concepts: fitness - introduction, Baye's rule, probability distributions, information theory, appendix: laws of probability, notes, exercises; programs - introduction, heuristic search, two-person games, biological state spaces, notes, exercises; data - data compression, coordinate systems, Eigenvalues and Eigenvectors, random vectors, high-dimensional spaces, clustering, appendix: linear algebra review, notes, exercises; dynamics - overview, linear systems, nonlinear systems, appendix: taylor series, notes, exercises; optimization - introduction minimization algorithms, the method of Lagrange multipliers, optimal control . Part 2 Memories: content-addressable memory - introduction, Hopfield memories, Kanerva memories, radial basis functions, Kalman filtering, notes, exercises; supervised learning - introduction, perceptions, continuous activation functions, recurrent networks, minimum description length, the activation function, notes, exercises; unsupervised learning - introduction, principal components, competitive learning, topological constraints, supervised competitive learning, multimodal data, independent components, notes, exercises. Part 3 Programs: Markov models - hidden Markov models - notes, exercises; reinforcement learning - introduction, Markov decision process, the Core ideas - policy improvement, Q-learning, temporal-difference learning, learning with a teacher, partially observable MDPs, summary, notes, exercises. Part 4 Systems: genetic algorithms - introduction, schemata, determining fitness; genetic programming - introduction, genetic operators for programs, genetic programming, analysis, modules, summary; summary - learning to react - memories, learning during a lifetime - programs, learning across generations - systems, the grand challenge revisited, note.
