1 The Monte Carlo Method -- 1.1 The Metropolis algorithm -- 1.2 Other ensembles -- 1.3 A non-symmetrical underlying matrix -- 1.4 Molecular systems -- 1.5 Polymers -- 1.6 Conclusions -- 2 The Molecular Dynamics Method -- 2.1 Introduction -- 2.2 Constraint dynamics -- 2.3 Long-range forces -- 2.4 Conclusions -- 3 Back to basics -- 3.1 Introduction -- 3.2 Average values, fluctuations and errors -- 3.3 Ensembles and distributions -- 3.4 Distribution functions -- 3.5 Finite-size scaling and phase transitions -- 3.6 Transport coefficients -- 3.7 Conclusions -- 4 Advanced Monte Carlo Techniques -- 4.1 Introduction -- 4.2 Other ensembles -- 4.3 Virtual moves -- 4.4 Beyond single-particle moves -- 4.5 Thermodynamic integration -- 4.6 The grand canonical miracle -- 5 Thermodynamic Constraints -- 5.1 Introduction -- 5.2 Andersen's demon -- 5.3 Time and ensemble averages -- 5.4 Nosé-Hoover thermostat -- 5.5 Molecular systems -- 5.6 Numerical considerations -- 6 Computer Simulations in the Gibbs Ensemble -- 6.1 Introduction -- 6.2 Theoretical aspects -- 6.3 Computational aspects -- 6.4 Finite-size effects -- 6.5 Analyzing the results -- 6.6 Applications -- 7 Effective Pair Potentials and Beyond -- 7.1 Introduction -- 7.2 Where potentials come from -- 7.3 Simple atomic and ionic systems -- 7.4 Molecular liquids and solids -- 7.5 Introducing electronic polarization -- 7.6 Bonded interactions -- 7.7 Conclusion -- 8 First-Principles Molecular Dynamics -- 8.1 Introduction -- 8.2 First-principles interatomic potential -- 8.3 Molecular dynamics based on density functional theory -- 8.4 Practical implementation of a DF-MD scheme -- 8.5 Selected applications of first-principles molecular dynamics -- 9 Computer Simulation Methods for Nonadiabatic Dynamics in Condensed Systems -- 9.1 Introduction -- 9.2 Formal background and basic principles -- 9.3 Surface-hopping trajectory methods -- 9.4 Self-consistent energy-conserving nonadiabatic dynamics -- 9.5 Test problems and applications -- 9.6 Conclusions -- 10 Long Length-Scale Aspects of Self Organization Phenomena -- 10.1 Introduction: an apologia -- 10.2 Universality and near universality -- 10.3 The nearest-neighbour Ising model as a computational model of simple fluids -- 10.4 Wheeler-Widom-type models as lattice fluids -- 10.5 Some tricks of the trade; Ising-like models -- 10.6 Analysis of Monte-Carlo data: finite-size scaling in Monte-Carlo simulations -- 10.7 Equilibrium simulation of Ising lattice model of microemulsion and the systematic construction of the phase diagram of microemulsion -- 10.8 Conclusions -- 11 Computer Simulation of Polymers -- 11.1 Introduction -- 11.2 Polymer simulations: general considerations -- 11.3 Algorithms for static properties -- 11.4 Simulations of polymer dynamics -- 11.5 Reptation -- 11.6 Extensions: glasses and networks -- 11.7 Further reading -- 11.8 Conclusions -- 12 Computer Simulations of Surfactants -- 12.1 Introduction -- 12.2 Surfactant monolayers -- 12.3 Micelles -- 12.4 Concluding remarks -- 13 Parallel Computing and Molecular Dynamics Simulations -- 13.1 Introduction -- 13.2 Computer architectures -- 13.3 Parallel programming concepts -- 13.4 Parallel molecular dynamics -- 13.5 Nucleation simulations -- 13.6 Conclusion -- 14 Scientific Visualization, A User View -- 14.1 Introduction -- 14.2 Discussion -- List of Participants -- List of Posters.