Browse Hierarchy DAT6501: AI and Statistical Data Analysis

In this module, you will gain familiarity with the foundations of data analysis and learn about some recent developments in artificial intelligence. The aim is to give you an overview and practical introduction to the most widely-used machine learning and statistical inference techniques for data description, prediction and explanation, and to help you apply them in business contexts. The module is taught through a combination of lectures on the theory and operation of modern techniques, and computer lab projects where you will implement a variety of methods to solve business problems. Indicative content: - Probability ¿ risks, likelihoods; conditional probability, distribution functions, expectations - Statistics ¿ descriptive statistics, estimators, confidence intervals, hypothesis tests - Data models ¿ classification, regression; overfitting; training, cross-validation, accuracy - Unsupervised and exploratory methods ¿ clustering, dimension-reduction, factor analysis - Predictive methods ¿ decision trees, kNN, support vector machines, random forest; bagging & boosting; artificial neural networks and their architectures; overview of LLMs - Explanatory methods ¿ linear modelling, time-series models, causal inference - Data ethics ¿ human values, rights, bias, regulatory & legal frameworks - Results presentation; communication with stakeholders; decision-support.