An International Universityin the Heart of Italy
An International UniversityPostgraduate · Master
A US graduate degree in artificial intelligence delivered in partnership with Westcliff University (California) — a blended, English-taught programme building advanced expertise in machine learning, deep learning, NLP, computer vision and responsible AI.
Artificial intelligence is reshaping every industry — from healthcare and finance to manufacturing and the creative economy. The MSAI builds the mathematical, statistical and engineering foundations to design, train and deploy modern AI systems, with explicit attention to ethics, safety and production-grade ML practice.
Mathematical and statistical foundations, classical and deep machine learning, neural architectures, NLP, computer vision, reinforcement learning and the production engineering required to ship AI to real users.
Graduates in computer science, engineering, mathematics, physics or quantitative disciplines — and working professionals who want to specialise in machine learning, applied AI or AI research.
A US graduate qualification aligned with WSCUC standards, a portfolio of applied ML projects and a capstone research thesis — ready for ML engineering, data science leadership or doctoral study (DCS, DDSci, DIT).
The MSAI follows the Westcliff graduate AI framework. Modules progress from mathematical and statistical foundations, through the core families of modern machine learning, into specialised domains and production practice, culminating in an original capstone AI research project.
Linear algebra, multivariable calculus, probability and optimisation theory rebuilt around the operations that drive modern machine learning. Students learn to read, derive and reason about the equations behind every algorithm in the curriculum.
Probabilistic modelling, inference, hypothesis testing and the bias–variance trade-off as the formal language of learning from data. Covers regression, classification and resampling, with rigorous evaluation of model uncertainty.
The classical ML toolkit — linear and logistic models, decision trees, ensembles, kernel methods, clustering and dimensionality reduction — with hands-on implementation, model selection and end-to-end pipeline design on real datasets.
Backpropagation, modern optimisers, regularisation and the practical engineering of training neural networks at scale. Students implement architectures from scratch and in PyTorch, and learn to debug and accelerate training.
The architectural zoo of contemporary AI — CNNs, RNNs, attention and transformer families, autoencoders and generative models. Emphasis on inductive bias, architectural choice and the engineering of large-scale models.
From tokenisation and embeddings to large language models and instruction-tuned systems. Covers sequence modelling, transformers, retrieval-augmented generation and the evaluation and alignment of modern LLMs.
Image representation, convolutional and vision-transformer architectures, detection, segmentation and multimodal vision–language models. Includes practical work on real visual data and modern foundation-model fine-tuning.
Markov decision processes, value- and policy-based methods, deep RL and reinforcement learning from human feedback (RLHF). Students design agents that learn from interaction and explore applications from games to robotics.
Fairness, accountability, transparency, privacy and safety in AI systems — analysed through case studies, regulatory frameworks (EU AI Act, NIST AI RMF) and the technical methods used to audit and mitigate model harms.
Versioning, reproducibility, CI/CD for ML, feature stores, model serving, monitoring and drift detection. Students ship a containerised model to a cloud environment and learn how AI systems are operated in production.
An individual research project, supervised by faculty, in which the student frames an open AI problem, conducts a rigorous experimental study and defends the work before a committee — producing a portfolio-grade research artefact.
The MSAI is taught by Westcliff University faculty and Unicollege academic staff — a mix of researchers, applied scientists and ML engineers active in industry and academia.
"Building AI systems is no longer about a single clever model — it is about disciplined mathematics, honest evaluation, and engineering that holds up in production. The MSAI is designed to teach all three." — MSAI Programme Director, in partnership with Westcliff University
Graduates step into senior technical and research roles across AI labs, technology firms and AI-driven business functions — in Italy, the EU and internationally.
Designs, trains and ships ML models into production systems at scale.
Advances the state of the art in industrial AI labs, R&D centres or doctoral programmes.
Builds analytical models and decision systems from real-world data for business and policy.
Develops language understanding, conversational and LLM-powered systems for products at scale.
Builds perception systems for imaging, robotics, autonomous platforms and creative tooling.
Leads cross-functional teams turning AI capabilities into validated, ethical, shipped products.