Adaptive computation and machine learning 803

Module code NEP 803
Qualification Postgraduate
Faculty Faculty of Natural and Agricultural Sciences
Module content

Introduction: Basic concepts. Supervised learning setup: Least means squares, logistic regression, perceptron, exponential family, generative learning algorithms, Gaussian discriminant analysis, naïve Bayes, support vector machines, model selection and feature selection. Learning theory: bias/variance tradeoff, union and Chernoff/Hoeffding bounds, VC dimension, worst case (online) learning. Unsupervised learning: clustering, k-means, expectation maximisation, mixture of Gaussians, factor analysis, principal components analysis, independent components analysis. Reinforcement learning and control: Markov decision processes, Bellman equations, value iteration and policy iteration, Q-learning, value function approximation, policy search, reinforce, partially observable Markov decision problems.

Module credits 15.00
Prerequisites No prerequisites.
Language of tuition Module is presented in English
Department Statistics
Period of presentation Semester 1 or Semester 2

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