Artificial Intelligence Engineering

Machine learning, deep learning, generative AI and MLOps.

AI Foundations & Maths

• Introduction to AI Engineering & the ML Workflow
• Matrices & Determinants
• Eigenvalues & Eigenvectors
• Partial Differentiation
• Probability Fundamentals & Bayes
• Descriptive Statistics
• Unconstrained Optimisation

Machine Learning

• Linear & Logistic Regression with Gradient Descent
• Classical ML: Trees, Ensembles, k-NN, SVM & Clustering
• Model Evaluation, Validation & Metrics

Deep Learning

• Neural Networks & Backpropagation
• Deep Learning: CNNs, RNNs, Optimisers & Training at Scale

Transformers & Generative AI

• Transformers & Large Language Models
• Generative AI Applications: Prompting, RAG & Agents

MLOps & Responsible AI

• MLOps, Deployment & Responsible AI
• Healthcare Data Science & Medical AI