Machine Learning And Computational Statistics

Module Title:
Machine Learning and Computational Statistics
Module Code:

Module Content
  • Statistical learning theory framework, stochastic gradient descent,
  • Matrix/vector differentiation
  • Excess risk decomposition, L1/L2 regularisation, Lasso algorithms, sub-gradient descent
  • Loss functions, convex optimization, Support Vector Machine
  • ernels, kernel ridge regression, kernelised Support Vector Machine
  • Trees, bias and variance decomposition
  • nsemble methods: bootstrap, bagging, random forest, AdaBoost
  • Gradient boosting, neural networks Spring Break
  • Natural exponential families and generalised linear models
  • Bayesian networks, class-conditional models, naïve Bayes
  • Clustering, Gaussian mixture models, EM algorithm
  • Bayesian methods, hierarchical models, Gibbs sampling, Singular Value Decomposition, PCA, Linear Discriminant Analysis