As large language models (LLMs) like ChatGPT continue to dominate AI applications, there is a growing tendency to view them as one-size-fits-all solutions for reasoning, decision-making, and even software development. However, at the core of robust machine learning systems lies a critical concept, Probabilistic Machine Learning. This talk will explore why probabilistic reasoning remains fundamental, even in an era where deep learning and transformer-based models seem to overshadow traditional statistical methods.

We will discuss how probabilistic modelling is a key pillar in machine learning, supporting essential components such as causality, uncertainty estimation, statistical inference, and structured decision-making. The talk will highlight how Probabilistic ML is integral in areas like Bayesian inference, predictive modelling, and reinforcement learning and in improving LLMs' reliability, interpretability, and ability to handle uncertainty. Attendees will learn why fundamental probabilistic principles remain essential for building truly intelligent, explainable, and trustworthy AI despite the rise of LLMs.