- 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.
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, basic concepts and motivation.
- Data pre-processing: handling missing values, basic data transformations.
- Rule induction; decision trees; naïve Bayesian probability; neural networks.
- Advanced topic 1: image processing
- Perceptron and support vector machines.
- Ensemble methods: boosting, bagging & random forests.
- Evaluation: cross validation, ROC.
- Lazy learning: clustering and rule mining; association rule mining.
- Time series.
- Advanced topic 2: text mining with feature engineering; vector space models.
- Advanced topic 3: graph mining.
- Advanced topic 4: TBC