Students master scientific research principles: formulating research questions, systematic literature review, experiment design, avoiding plagiarism, and following research ethics standards — underpinning the entire research-focused arc of the minor.

Students gain solid theoretical and practical grounding in classical ML algorithms — regression, classification, clustering, dimensionality reduction — and foundational AI concepts including search, knowledge representation, and probabilistic reasoning.

Students understand and implement deep neural networks — CNNs, RNNs, Transformers — and generative models including VAEs, GANs, and diffusion models. They can train and critically evaluate modern generative AI systems on real datasets.

Students develop proficiency in the mathematical backbone of ML: gradient descent variants, convex optimisation, constrained optimisation (KKT conditions), evolutionary algorithms, and meta-heuristics for complex AI problems.

Students independently survey AI/ML literature in a chosen sub-domain, critically synthesise findings, identify open research gaps, and produce a publication-quality review paper — developing scholarly communication and independent research competency.