Technology and Artificial Intelligence Course

Lesson 1 • How Digital Systems Work

Covers hardware, software, and network layers that underpin all AI systems. Establishes the technical vocabulary used throughout the course.

Lesson 2 • History and Evolution of AI

Traces AI from rule-based systems to modern deep learning. Provides historical context that explains current capabilities and limitations.

Lesson 3 • Data as the Core Resource

Explains structured, unstructured, and semi-structured data types and their roles in AI. Connects data quality directly to model performance.

Lesson 1 • Data Sources and Acquisition Methods

Surveys primary, secondary, and synthetic data sources and collection techniques. Grounds students in where real-world AI data originates.

Lesson 2 • Data Storage and Pipeline Design

Covers databases, data warehouses, and automated pipelines for continuous data flow. Ensures students can design scalable data infrastructure for AI projects.

Lesson 3 • Data Cleaning and Preprocessing

Teaches detection and correction of missing values, outliers, and inconsistencies. Directly improves downstream model accuracy and reliability.

Lesson 1 • Unsupervised Learning Techniques

Introduces clustering, dimensionality reduction, and anomaly detection without labelled data. Expands students' ability to extract patterns from unlabelled datasets.

Lesson 2 • Supervised Learning Algorithms

Covers regression, classification trees, and support vector machines with worked examples. Builds the algorithmic toolkit used in most production ML systems.

Lesson 3 • Model Training and Optimization

Explains loss functions, gradient descent, and hyperparameter tuning. Equips students to improve model performance systematically.

Lesson 4 • Model Evaluation and Selection

Teaches accuracy, precision, recall, F1, AUC-ROC, and cross-validation for fair model comparison. Prevents overfitting and underfitting in deployed systems.

Lesson 1 • Neural Network Fundamentals

Explains neurons, layers, activation functions, and forward propagation. Provides the mathematical intuition needed for all deep learning topics ahead.

Lesson 2 • Convolutional Neural Networks

Covers convolution, pooling, and feature map extraction for image data. Enables students to build image classifiers and object detectors.

Lesson 3 • Training Deep Models Effectively

Addresses batch normalisation, dropout, early stopping, and GPU utilisation. Reduces training failures and accelerates convergence in practice.

Lesson 1 • Fine-Tuning and Domain Adaptation

Covers supervised fine-tuning, parameter-efficient methods, and domain-specific datasets. Prepares students to customise LLMs for specialised organisational needs.

Lesson 2 • Prompt Engineering and Generative Applications

Develops skills in crafting, iterating, and evaluating prompts for generative AI outputs. Enables students to deploy LLMs in real workflows without retraining.

Lesson 3 • Transformer Architecture and Large Language Models

Explains attention mechanisms, transformer blocks, and pre-trained LLM families. Gives students the conceptual foundation to use and fine-tune LLMs.

Lesson 4 • Text Representation and Preprocessing

Covers tokenisation, stemming, embeddings, and TF-IDF for converting text to numbers. Establishes the input pipeline for all NLP models.

Lesson 1 • Multimodal AI Systems

Integrates vision and language into unified models for captioning, visual QA, and search. Prepares students for the growing class of cross-modal AI products.

Lesson 2 • Semantic and Instance Segmentation

Teaches pixel-level labelling for scene understanding and medical imaging use cases. Extends object detection skills to finer-grained spatial analysis.

Lesson 3 • Image Classification and Object Detection

Builds on CNN knowledge to implement classifiers and detectors on real image datasets. Connects deep learning theory to practical vision pipelines.

Lesson 1 • Scaling AI Infrastructure

Addresses distributed training, auto-scaling, and cost optimisation for large workloads. Prepares students to manage AI systems at enterprise scale.

Lesson 2 • CI/CD Pipelines for ML

Adapts continuous integration and delivery practices to ML model updates. Enables reliable, automated model releases with minimal manual intervention.

Lesson 3 • Monitoring and Model Drift Detection

Teaches data drift, concept drift, and performance degradation detection in production. Ensures deployed models remain accurate over time.

Lesson 4 • Model Packaging and Serving

Covers serialisation formats, REST APIs, and containerisation for model serving. Bridges the gap between trained models and live production systems.

Lesson 1 • Explainability and Transparency

Teaches SHAP, LIME, and model cards for communicating AI decisions to stakeholders. Builds trust and supports accountability in high-stakes applications.

Lesson 2 • Bias Sources and Fairness Metrics

Maps bias origins from data collection through model output and quantifies them with fairness metrics. Grounds ethical analysis in measurable, technical terms.

Lesson 3 • Privacy, Security, and Data Rights

Addresses differential privacy, federated learning, and data minimisation for protecting individuals. Connects technical controls to privacy obligations.

Lesson 4 • Bias Mitigation Techniques

Covers pre-processing, in-processing, and post-processing debiasing methods. Equips students to reduce discriminatory outcomes without sacrificing accuracy.