AI and Machine Learning

This module provides a structured and comprehensive introduction to modern Artificial Intelligence (AI) and Machine Learning (ML). Students develop a solid understanding of the concepts, the mathematical and computational underpinnings, and the operational components of state of the art AI and ML techniques, tools, and enabling infrastructures. The course is designed to equip students with the ability to critically evaluate and competitively manage AI systems, henceforth understand how models are developed, deployed, and governed, and assess their implications in real-world organizational and societal contexts. The module coverage spans from classical ML approaches (e.g. linear models, decision trees) to advanced deep learning architectures and generative AI and foundation models, highlighting both strengths and limitations of different model families. Students learn how AI-driven solutions are designed and managed across the full ML lifecycle, from data acquisition and preprocessing to model training, validation, deployment, and monitoring, while accounting for data governance, quality, ethical, and socio-technical constraints. A key perspective of the module design is to offer students the knowledge to foster the integration of AI into business strategy and operations. More specifically, the module will enable students to co-design and evaluate AI-enabled products and services, and lead or contribute to AI-driven transformation initiatives across diverse sectors and functional roles such as in finance, operations, marketing, and strategy or policy. The module employs examples/exercises, paper discussions, and state of the art pedagogical approaches (e.g. group work and flipped classroom activities). Besides the theoretical aspects, the students will be introduced to the associated tools/development platforms per Leacture topic. e.g., Python Libraries, PyTorch/TensorFlow/Keras, Enterprise and Cloud Platforms