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Module Title: 
Machine Learning and Computational Statistics
Module Code: 
DSC6135

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

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African Center of Excellence in Data Science(ACE-DS)

Head Office:UR College of Business & Economics

E-mail:   aceds@ur.ac.rw

Phone:(+250) 788484421