Projects of the "Numerical simulation" course which I have taken on my third year of Bachelor degree. The course is divided in 12 excercises. In the following lines I explain which arguments are discussed in each exercise:
1.Random Number Generators and Central Limit Theorem.
2.Monte Carlo integration, Importance sampling, Markov processes.
3.Black-Scholes Theory.
4.Molecular Dynamics Simulations: Verlet algorithm.
5.Metropolis algorithm.
6.The Ising model simulation: Metropolis and Gibbs sampling.
7.Molecular Dynamics Simulations: Metropolis algorithm.
8.Path Integral Monte Carlo: Imaginary time evolution.
9.Heuristic optimization: Genetic algorithms and Simulated annealing.
10.Genetic algorithm with parallel computing: Message Passing Interface.
11.Feed-forward Neural Networks for supervised learning.
12.Convolutional Neural Networks for image recognition.