Curriculum

This course introduces the foundational data structures and algorithmic principles required for building efficient AI systems. The focus is on computational thinking, algorithmic efficiency, and scalable problem-solving techniques. Students learn how to represent, store, receive, and process data efficiently such as graphs, trees, and hash-based systems.

This course builds Python programming competency for students entering the MS in AI programme. Starting from core language fundamentals, the course progresses through scientific computing with NumPy, data manipulation with Pandas, visualization with Matplotlib, and the applied ML and AI ecosystem including PyTorch and the HuggingFace libraries. Equal emphasis is placed on writing well-structured, readable code and on developing the practical skills that underpin all subsequent programme coursework. The course is delivered in two parts: the first builds the Python foundation required for the programme's ML and AI theory courses; the second applies that foundation to frameworks and tooling used in modern machine learning practice.

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This course introduces students to the principles of data management systems that power modern AI applications. It covers relational database design, SQL-based querying, normalization, transactions, indexing, and query optimization, along with NoSQL systems and vector database extensions used in AI. Students learn how data is structured, stored, queried, and retrieved efficiently, with emphasis on building reliable, scalable, and query-efficient data systems, including similarity search over vector embeddings that support machine learning and generative AI applications.

This two-week course covers the foundations of modern machine learning. Students begin with supervised learning fundamentals, including k-nearest neighbors, linear and logistic regression, perceptrons, and gradient descent. The course then progresses through support vector machines, kernel methods, Gaussian processes, and tree-based models including decision trees, random forests, and boosting. The second week shifts to neural networks, unsupervised learning (clustering, GMMs, PCA), and sampling, before concluding with contemporary topics: generative modeling and diffusion, transformers and large language models, and reinforcement learning

This course provides a hands-on introduction to large language models and generative AI. Students will learn how modern AI systems work, from the transformer architecture and training process through to practical applications including retrieval-augmented generation, tool use, and AI agents. Topics include LLM APIs and prompting, chain-of-thought reasoning, function calling, embeddings and vector search, RAG pipelines, the ReAct agent paradigm, fine-tuning and RLHF, large reasoning models, and multimodal AI. The course emphasizes both conceptual understanding and practical skills through five programming assignments.

This course provides a comprehensive, hands-on foundation in the core engineering principles required to build, evaluate, deploy, and maintain robust machine learning workflows. Moving beyond theoretical algorithms, students will focus on the practical end-to-end lifecycle of ML systems—from data preparation and rigorous model evaluation to deployment mechanics and post-deployment monitoring. By the conclusion of this course, learners will transition from data science practitioners to machine learning engineers capable of building reproducible, production-ready pipelines.

This course focuses on practical data handling, cleaning, transformation, and visualization techniques essential for AI and data science workflows. Students learn how to explore datasets, identify patterns, engineer features, and communicate insights effectively using visualization tools.

The course emphasizes data storytelling and prepares students for building robust datasets for machine learning and generative AI systems.

This is a project-based integrative course where students build their first end-to-end AI system using knowledge from programming, mathematics, data structures, databases, and introductory machine learning.

Students work in teams to design, develop, and present a working AI application involving data ingestion, preprocessing, modeling, evaluation, and basic deployment. The course emphasizes real-world problem-solving, collaboration, and system-level thinking.

AI capability commoditises faster than the advantage built on it forms, so the firm that creates the value is often not the firm that keeps it. Advantage lives in a loop that accumulates only while the system is operated and decays when it is neglected. Systems fail probabilistically, and agentic systems act before anyone reviews the action, which makes the boundary between human and machine authority a strategy question. The stack concentrates in a few model, cloud and compute providers, so every sourcing decision is also a dependency decision.

Durable advantage comes from cognitive capital, meaning the situated judgement an organisation accumulates through overrides, escalations, adjudications and outcomes. Any competitor can license the same model. None of them can license the judgement a firm has built by running that model against its own work for two years. The most durable returns come from boring AI, meaning advanced technology pointed at routine, high frequency work, and the showcase projects that attract budget and headlines rarely produce them. Through 2025 the large majority of enterprise AI initiatives stalled in pilot purgatory, somewhere between a convincing demonstration and a system that carries real work, and the course is an account of why that happens.

Classical strategy enters at the point of use. Rumelt’s strategy kernel, Porter’s Five Forces, transaction cost economics and the resource based view are applied to specific AI decisions and stress tested where AI economics strain their assumptions. Each team carries one assigned firm through all 10 lectures, producing five decision canvases that accumulate into a strategy addressed to the board of a listed firm or to the investors of a private one, and defended in the final session.

The course is built for students who can already build AI systems and who will shortly be asked to decide which ones deserve to exist.

Running question. You can build it, so should it be built, who captures what is created, and what makes it last after it works?