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CS909 Data Mining

CS909 15 CATS Term 2


Core - MSc Data Analytics, MSc Behavioural and Data Science.
Option - MSc Computer Science, Year 4 MEng CS and DM


No Warwick module is required as pre-requisite. However familiarity with basic probability and statistics (for example: discrete and continuous random variables, densities and distributions, common distributions including Bernoulli, binomial, uniform and normal distribution, expectations) will be needed.

Academic Aims

• Understanding of the value of data mining in solving real-world problems.
• Understanding of foundational concepts underlying data mining.
• Understanding of algorithms commonly used in data mining tools.
• Ability to apply data mining tools to real-world problems.

Learning Outcomes

By the end of the module, the student should

  • Display a comprehensive understanding of different data mining tasks and the algorithms most appropriate for addressing them.
  • Evaluate models/algorithms with respect to their accuracy.
  • Demonstrate capacity to perform a self directed piece of practical work that requires the application of data mining techniques.
  • Critique the results of a data mining exercise.
  • Develop hypotheses based on the analysis of the results obtained and test them.
  • Conceptualise a data mining solution to a practical problem.


  • Introduction to machine learning, basic concepts and motivation.
  • Data pre-processing and basic data transformations.
  • Regression models (linear regression, logistical regression.
  • Classification: decision trees, probabilistic generative models
  • Model evaluation, bias-variance trade-off
  • Ensemble methods: boosting, bagging & random forests.
  • Dimensionality reduction: Principal Component Analysis (PCA), T-distributed Stochastic Neighbour Embedding (t-SNE).
  • Introduction to deep learning, backpropagation, gradient descent
  • Convolutional neural networks
  • Word embeddings
  • Sequence-to-sequence models
  • Attention mechanisms and memory networks
  • Unsupervised deep learning and generative models
  • Transfer learning


  • Bishop, C (2008) Pattern Recognition and Machine Learning, Springer
  • Goodfellow, I, Bengio, Y and Courville, A (2016). Deep Learni, MIT Press
  • Leskovec, J, Rajaraman, A & Ullman, J.D. (2014). Mining of massive datasets. Cambridge university press.
  • Tan, P, Steinbach, M, Karpatne, A, Kumar, V. (2019), Introduction to Data Mining, 2nd Edition
  • Murphy, K. P. Machine learning: a probabilistic perspective. The MIT Press.


Two hour examination (50%), coursework (50%) - MEng Students

Two hour examination (40%), coursework (60%) - MSc students


20 one-hour lectures:

8 one-hour labs

5 one-hour seminars