Part I: Fundamentals and Reviews
第一週 Introduction to Basic Statistics, Matrix Algebra and Statistical Computing
Part II: Linear Algebraic Techniques
第二週 Matrix Decomposition Methods
第三週 Eigenvalue Problems
第四週 Principal Component Analysis and Power Iteration Method
第五週 Singular Value Decomposition and Low-Rank Tensor Recovery
第六週 Independent Component Analysis and Gaussianity
Part III: Statistical Computing Techniques
第七週 Simulation, Sampling and Monte Carlo Methods
第八週 EM Algorithms and Methods of Bootstrapping
第九週 MCMC: Metropolis-Hastings and Gibbs Sampling
第十週 Nonparametric Density Estimation and Multivariate Smoothing
Part IV: Modern Methods in Optimization
第十一週 Traditional Methods: Linear Programming and Gradient Methods
第十二週 Stochastic Local Searches: Simulated Annealing and Tabu Search
第十三週 Genetic Algorithms: Standard Framework and Evolution Strategy
第十四週 Group Intelligence: Particle Swarm Optimization and Ant Colony Optimization
Part V: Advanced Topics in Optimization
第十五週 Coevolution: Competitive Coevolution and Cooperative Coevolution
第十六週 Multi-Objective Optimization Techniques
第十七週 Other Advanced Topics in Optimizations
第十八週 期末考週