Artificial Intelligence

Learn to build autonomous AI agents that can perceive, reason, and act in complex environments. Covers agent architectures, planning algorithms, multi-agent systems, and real-world applications in robotics, gaming, and automation.

This topic examines the moral, legal, and social implications of artificial intelligence technologies. Learners will understand how to identify bias, ensure fairness, protect privacy, and implement governance frameworks to guide responsible AI development and deployment.

This topic introduces the core concepts, history, and branches of artificial intelligence, including search, logic, and probabilistic reasoning. Learners will understand the foundational theories and architectures that drive modern intelligent systems and automated decision-making.

AI policy covers how governments and institutions govern artificial intelligence. You will learn the EU AI Act, US and Chinese approaches, risk-based regulation, compute governance, liability and copyright questions, and how policy on frontier models is made.

AI safety studies how to make advanced AI systems behave as intended. You will learn alignment problems, interpretability, evaluations, governance debates and the main research agendas and their critiques.

AI coding assistants change how software is written. You will learn to work effectively with code-generation tools and agents, review their output, structure prompts and repos for them, and keep quality and security high.

This field covers the methods and technologies used to process, analyze, and extract insights from massive, complex datasets. Learners will understand how to utilize distributed computing frameworks and storage systems to handle high-volume and high-velocity data.

This field of artificial intelligence enables computers to derive meaningful information from digital images, videos, and other visual inputs. Learners will understand how to implement image processing, object detection, and image segmentation algorithms.

This multidisciplinary field combines statistics, scientific computing, and algorithms to extract knowledge and insights from structured and unstructured data. Learners will understand how to perform data cleaning, exploratory analysis, predictive modeling, and data visualization.

This subset of machine learning utilizes multi-layered artificial neural networks to model complex patterns in data. Learners will understand how to design, train, and evaluate deep neural architectures for tasks like image recognition and natural language processing.

Fine-tuning adapts a pretrained language model to a task or style. You will learn supervised fine-tuning, LoRA and parameter-efficient methods, preference optimisation, datasets and evaluation.

GPU programming runs computations across thousands of parallel cores. You will learn the CUDA programming model, the GPU memory hierarchy, writing kernels, parallel patterns such as reduction and convolution, and how GPUs accelerate machine learning and scientific computing.

Diffusion and related models generate images and video from text. You will learn how they work, how to run and prompt them, control techniques like ControlNet and LoRA, and the creative and legal issues around them.

LLM evaluation measures whether a language-model application actually works. You will learn error analysis, building test sets, code-based and model-graded checks, benchmarks and their limits, and how to monitor quality once a system is in production.

Explore the world of large-scale language models like GPT, BERT, and their variants. Learn about pre-training, fine-tuning, prompt engineering, and deploying LLMs for various applications.

This practice focuses on unifying machine learning system development and operations to standardize the deployment and maintenance of models. Learners will understand how to build automated pipelines for continuous integration, delivery, and monitoring of machine learning workflows.

This discipline focuses on developing algorithms that allow computers to learn from and make predictions on data without explicit programming. Learners will understand how to apply supervised, unsupervised, and evaluation techniques to solve predictive problems.

Machine learning rests on linear algebra, calculus, probability and optimisation. You will learn exactly the mathematics needed to read ML papers and textbooks, with the resources that teach it in that order.

This field focuses on the interaction between computers and human language, enabling machines to process and analyze textual data. Learners will understand how to build systems for text classification, translation, sentiment analysis, and information extraction.

Understand the building blocks of deep learning. Study perceptrons, activation functions, backpropagation, optimization algorithms, and network architectures from basics to advanced concepts.

Philosophy of technology asks what machines can know, feel and owe us. You will learn debates on machine consciousness, the ethics of automation, technology's effect on society and the key thinkers.

Predictive analytics uses historical data, statistical algorithms, and machine learning to identify the likelihood of future outcomes. You will understand how to build forecasting models, analyze trends, and assess risks to guide strategic planning.

Prompt engineering is the craft of getting reliable results from large language models. You will learn prompting patterns, structured outputs, evaluation, and the limits of prompting versus fine-tuning or retrieval.

Recommender systems rank items for users at Netflix, Amazon and Spotify scale. You will learn collaborative filtering, matrix factorisation, deep and sequential models, evaluation and the feedback-loop pitfalls.

This area of machine learning focuses on how software agents ought to take actions in an environment to maximize cumulative rewards. Learners will understand how to design decision-making models using Markov decision processes and Q-learning algorithms.

RAG grounds language-model answers in your own documents. You will learn embeddings, vector search, chunking, reranking and evaluation, and how to build and debug a production retrieval pipeline.

Running open-weight language models on your own hardware keeps data private and costs predictable. You will learn model formats and quantization, memory and GPU requirements, tools such as llama.cpp and Ollama, and how to choose a model for a task.

AI tools are changing how people draft, edit and research. You will learn effective workflows for writing with language models, their limits and failure modes, and how to keep your voice and integrity.