Data Mining and Neural Networks

Module code: MA7022

Topics covered

  • Classification (knn and Decision tree algorithms)
  • Entropy and conditional entropy
  • Information gain, clustering (k-means, hierarchical clustering and density based algorithms)
  • Multivariate regression (linear regression and the kernel trick)
  • Probability distribution estimation (Bayes rule and Bayes networks – structure and algorithm of construction)
  • Preprocessing
  • Data cleaning
  • Dimension reduction (principal component analysis)
  • Time series, stationary time series (in strong and in weak sense), white noise, random walk, moving average processes
  • Autoregressive processes
  • Integrated and ARIMA processes
  • Smoothing -mean filter and median filter
  • Trend analysis and segmentation
  • Formal neurons
  • Perceptron learning rule and Widrow-Hoff learning rule
  • Main types of neural networks (Hopfield, Kohonen and back- propagation of errors) and problems they can solve
  • Cascade-correlation algorithms

Learning

  • 33 one-hour lectures
  • 11 one-hour seminars
  • 11 one-hour workshops
  • 95 hours of guided independent study

Assessment

  • Exam, two hours (50%)
  • Problem sheets (20%)
  • Computational tasks (30%)