Data Mining and Neural Networks

Module code: MA7022

Module co-ordinator: Alexander Gorban 


Classification (kNN and Decision tree algorithms), entropy, conditional entropy, and 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, autocorrelation function, 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, and cascade-correlation algorithms. 


  • 33 one-hour lectures
  • 11 one-hour seminars
  • 11 one-hour workshops


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