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I Introduction to Neural Networks; 1: General introduction; 2: Layered networks; 3: Recurrent networks with binary neurons; II Advanced Neural Networks; 4: Competitive unsupervised learning processes; 5: Bayesian techniques in supervised learning; 6: Gaussian processes; 7: Support vector machines for binary classification; III Information Theory and Neural Networks; 8: Measuring information; 9: Identification of entropy as an information measure; 10: Building blocks of Shannon’s information theory; 11: Information theory and statistical inference; 12: Applications to neural networks; IV Macroscopic Analysis of Dynamics; 13: Network operation: macroscopic dynamics; 14: Dynamics of online learning in binary perceptrons; 15: Dynamics of online gradient descent learning; V Equilibrium Statistical Mechanics of Neural Networks; 16: Basics of equilibrium statistical mechanics; 17: Network operation: equilibrium analysis; 18: Gardner theory of task realizability; Appendices; A: Historical and bibliographical notes; B: Probability theory in a nutshell; C: Conditions for central limit theorem to apply; D: Some simple summation identities; E: Gaussian integrals and probability distributions; F: Matrix identities; G: The delta-distribution; H: Inequalities based on convexity; I: Metrics for parametrized probability distributions; J: Saddle-point integration; References

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