According to the mathematician John Allen Paulos, “Uncertainty is the only certainty there is”, and indeed seeming or genuine randomness pops up just about everywhere from subatomic particles through stock prices to human decision making. Chance keeps unsettling and intriguing the scientific mind attracted to rigourous and determinate answers. Yet, from the machine learning revolution in computer science to processing protein expression data in molecular biology, from economic forecasting to decision theory, probabilistic thinking is ubiquitous in current academic discourse and in many cases. This module aims to establish a strong conceptual foundation of probability theory, while introducing students to some of the mathematical tools required in the field. Key concepts include risk, uncertainty, ambiguity, belief, evidence and likelihood. By the end of the module, students will be able to solve a range of probability related problems and reason in a rigorous manner about evidence or belief systems. The module is recommended for students interested in mathematics, computer science, economics, neuroscience and cognitive science, as well as those seeking further studies in fields that rely on statistical analysis, such as experimental psychology or quantitative social sciences.
Module Leader:Mavroyiannis Diomides