Technology
AI and Machine Learning
Build Intelligent Systems. Lead the Data Revolution.
Programme Overview
AI and Machine Learning
Five progressive levels from Foundation to Graduate Diploma. Each level is a complete, certified qualification. Exit at any level with a recognised certificate, or complete the full programme.
Five Levels. One Programme. Exit at Any Stage.
Programme Structure
Every level includes hands-on labs in every module, a 50-question final assessment and a final project. You may exit at any level with a recognised certificate, or continue to Graduate Diploma.
Modules (6)
1
Welcome to AI and the Digital World
This module covers What is artificial intelligence, really; How computers think; AI all around us; Ethics, bias, privacy and responsible AI. Learners finish able to explain AI, ML and data, and use AI responsibly.
2
Computer and Data Foundations
This module covers Files, folders and the cloud; Spreadsheets as your first data tool; What is data; Reading charts and telling a story. Learners finish able to organise data, use spreadsheets, and read charts.
3
First Steps in Python
This module covers Setting up Python and your first program; Variables, numbers and text; Conditions and comparisons; Loops and simple lists. Learners finish able to write basic Python with variables, decisions and loops.
4
Working with Data in Python
This module covers Lists and collections of data; Functions; Reading data from a file (CSV); Simple data analysis with pandas. Learners finish able to use functions, and load and analyse a dataset.
5
How Machine Learning Works
This module covers Learning from examples; Features, labels and training data; Your first model; Accuracy, testing and overfitting. Learners finish able to explain how a model learns and is judged, and train a simple model.
6
Build, Reflect and Plan
This module covers No-code and low-code AI tools; Prompting and generative AI; Your mini-project; Careers and your next level. Learners finish able to use AI tools, complete a project, and plan the next level.
Modules (8)
1
Data and Python Refresher for ML
This module covers Your machine learning workspace; Python for data, a fast refresher; pandas in depth; NumPy and arrays. Learners finish able to use Python, pandas and NumPy for data.
2
Data Wrangling and Preparation
This module covers Cleaning, missing values and duplicates; Transforming types, dates and text; Encoding categorical data; Feature scaling. Learners finish able to prepare data for modelling.
3
Exploratory Data Analysis
This module covers Descriptive statistics; Visualising with matplotlib and seaborn; Correlation and patterns; From analysis to insight. Learners finish able to explore data and draw insight.
4
Supervised Learning, Regression
This module covers What regression is; Linear regression with scikit-learn; Error metrics, MAE, MSE and R squared; Train, test and data leakage. Learners finish able to build and evaluate regression models.
5
Supervised Learning, Classification
This module covers What classification is; Logistic regression and decision trees; Confusion matrix, precision and recall; A classification project end to end. Learners finish able to build and evaluate classification models.
6
Model Evaluation and Improvement
This module covers Overfitting and the bias-variance idea; Cross-validation; Tuning and grid search; Pipelines. Learners finish able to improve and validate models.
7
Unsupervised Learning and Modern AI
This module covers Clustering with k-means; Dimensionality and an introduction to PCA; An introduction to neural networks; Generative AI and large language models. Learners finish able to apply unsupervised learning, and explain modern AI.
8
Applied Project and Professional Practice
This module covers AI ethics, fairness and the LGPD; Deployment basics; The capstone machine learning project; Your machine learning career and the next level. Learners finish able to apply ethics, deploy, and deliver a project.
Modules (8)
1
Mathematics for ML, Foundations
This module covers Why mathematics matters for machine learning; Functions, graphs and notation; Vectors and the geometry of data; Matrices and operations. Learners finish able to use mathematical language for ML.
2
Linear Algebra for ML
This module covers Matrix multiplication and transformations; Systems, rank and independence; Eigenvalues and eigenvectors; The linear algebra behind PCA. Learners finish able to apply linear algebra, including PCA.
3
Calculus for Optimisation
This module covers Derivatives and slopes; The gradient and partial derivatives; Gradient descent by hand and in code; The chain rule and the backpropagation idea. Learners finish able to use calculus and gradient descent.
4
Probability and Statistics for ML
This module covers Probability foundations; Distributions that matter; Expectation, variance and covariance; Bayes theorem and Bayesian thinking. Learners finish able to reason about uncertainty.
5
Statistical Inference and Experiments
This module covers Sampling and estimation; Confidence intervals; Hypothesis testing; Designing a sound experiment. Learners finish able to draw sound conclusions from data.
6
The Mathematics Inside the Models
This module covers The cost function of linear regression; Logistic regression and maximum likelihood; Regularisation, the mathematics of overfitting; How a neural network learns, end to end. Learners finish able to explain the maths inside models.
7
Academic and Research Skills
This module covers Reading a machine learning research paper; Academic writing and structure; Citation, integrity and the literature review; Reproducible research and experiments. Learners finish able to read, write and work reproducibly.
8
The Research-Readiness Project
This module covers Framing a research question; Method and evaluation design; The Pre-Master research-readiness project; Applying for a Master's, portfolio and proposal. Learners finish able to deliver a research-readiness project.
Modules (9)
1
Advanced Applied Machine Learning
This module covers From models to systems, the applied ML lifecycle; Feature engineering at scale; Ensembles and gradient boosting; Imbalanced data and real-world evaluation. Learners finish able to build advanced applied ML models.
2
Deep Learning Foundations
This module covers Tensors and a deep learning framework; Building and training a neural network; Regularisation and optimisation for deep nets; Tuning, callbacks and reproducible training. Learners finish able to build and train deep networks.
3
Computer Vision
This module covers Images as data and convolutions; Convolutional neural networks; Transfer learning and fine-tuning; A computer vision project. Learners finish able to apply computer vision.
4
Natural Language Processing
This module covers Text as data, tokenisation and representation; Word embeddings and semantics; Transformers and attention, the idea; Using and fine-tuning a pretrained language model. Learners finish able to build NLP systems.
5
Generative AI and LLMs in Practice
This module covers Using LLMs as a developer; Retrieval-augmented generation (RAG); Fine-tuning and adapting LLMs; Evaluating, guarding and grounding LLM systems. Learners finish able to build generative AI systems.
6
MLOps and Deployment
This module covers From notebook to product, the MLOps mindset; Packaging and serving a model; Monitoring, drift and retraining; CI/CD and automation for ML. Learners finish able to deploy and automate models.
7
Data Engineering for ML
This module covers Data pipelines and ETL; Working at scale, batching and parallelism; Feature stores and data versioning; Cloud and storage foundations for ML. Learners finish able to engineer data pipelines.
8
Responsible AI, Governance and Security
This module covers Fairness and bias in practice; Explainability and interpretability; Privacy, the LGPD and data protection by design; Model risk, security and adversarial robustness. Learners finish able to build fair, safe AI.
9
Applied Capstone and Professional Practice
This module covers Scoping an applied AI project; Building and evaluating the solution; The Graduate Certificate capstone; Professional practice, portfolio and the path to Graduate Diploma. Learners finish able to deliver an applied AI system.
Modules (12)
1
Advanced Deep Learning
This module covers Sequence models and the road to attention; Inside the transformer, a deep dive; Modern architectures and training at scale; Transfer, adaptation and efficient fine-tuning. Learners finish able to command advanced deep learning.
2
Large Language Models in Depth
This module covers How LLMs are built, pretraining and data; Alignment, instruction tuning and RLHF; Evaluating large language models rigorously; Multilingual and domain adaptation. Learners finish able to build and evaluate LLMs.
3
Advanced Computer Vision
This module covers Object detection; Image segmentation; Generative vision, the idea; Video and multimodal vision. Learners finish able to apply advanced vision.
4
Generative AI Systems and Agents
This module covers Advanced retrieval and RAG at scale; LLM agents and tool use; Orchestration and multi-step workflows; Evaluating and securing agentic systems. Learners finish able to build and secure agents.
5
Reinforcement Learning
This module covers The reinforcement learning problem; Value-based methods, Q-learning; Policy-based methods; Applications and limits of reinforcement learning. Learners finish able to apply reinforcement learning.
6
Scaling and Performance Engineering
This module covers Distributed and parallel training; Model efficiency, quantisation, pruning and distillation; Inference optimisation and serving at scale; Hardware, accelerators and cost. Learners finish able to scale and optimise systems.
7
Advanced MLOps and Production Systems
This module covers Production architecture for ML systems; Feature platforms and real-time ML; Observability, reliability and incident response; Continuous training and experimentation platforms. Learners finish able to architect production ML.
8
Data-Centric AI
This module covers Data quality and the data-centric mindset; Labelling, annotation and active learning; Synthetic data and augmentation at scale; Streaming data and online learning. Learners finish able to practise data-centric AI.
9
AI Safety, Governance and Regulation
This module covers Advanced fairness, robustness and alignment; Privacy-preserving machine learning; Regulation and standards; Auditing, assurance and risk management. Learners finish able to govern AI responsibly.
10
Research Methods and Innovation
This module covers Reading and critiquing frontier research; Designing rigorous experiments; Reproducibility and open science; Writing and communicating research. Learners finish able to engage with research.
11
AI Product, Strategy and Leadership
This module covers From model to product and value; Leading AI teams and projects; Ethics, trust and stakeholder communication; The economics and strategy of AI. Learners finish able to lead AI products.
12
The Major Capstone Project
This module covers Defining a substantial project; Literature, method and plan; The Graduate Diploma capstone; Defence, portfolio and your professional future. Learners finish able to deliver a major project.
Ready to Study AI and Machine Learning?
Three intakes: January, June and November. Florianopolis, Brazil.