Certificate Course in
AI, Machine Learning
& Cyber Security

Start with the meaning, evolution and everyday applications of AI.

• Narrow AI and applications across disciplines.
• AI versus machine learning, deep learning and generative AI.
• The basic AI workflow, limitations and challenges.

Practice: Identify and classify AI applications in different fields.

• Datasets, features, labels, data quality and data bias.
• Supervised, unsupervised and reinforcement learning.
• Training and testing data, overfitting and underfitting.
• Model evaluation: accuracy, precision, recall and F1-score.

Practice: Examine a dataset and explore classification and model evaluation with an accessible no-code tool.

• Machine learning workflows, classification, regression and clustering.
• Neural networks, neurons, layers and activation functions at a conceptual level.
• Deep learning, an introduction to convolutional neural networks and their applications.

Practice: Explore a visual no-code workflow and a simple neural network.

• Generative AI uses, strengths and limitations. AI assistants versus search engines.
• Tools for writing, study, research and creative work.
• Prompt framework: Role, Task, Context, Requirements and Output Format.
• Zero-shot, few-shot, role-based, step-by-step, context-rich, constraint-based, structured-output and iterative prompting.
• Hallucinations, outdated information, bias and fake references.
• Verify output using Generate, Check, Verify, Refine and Use.
• Academic integrity, disclosure, privacy and copyright.

Practice: Compare basic and structured prompts, refine outputs and check the reliability of AI-generated information.

• Assets, threats, vulnerabilities, risk and the CIA Triad.
• Authentication, authorisation, password security, multi-factor authentication and passkeys.
• Network, web, mobile and cloud security fundamentals.
• Practical cyber security habits.

Practice: Guided account-security and cyber-awareness exercises.

• Social engineering, phishing, smishing and vishing.
• Malware, ransomware and distributed denial-of-service attacks.
• Data breaches, identity theft, basic incident response and cyber fraud awareness.
• Safe online practices, taught at an awareness and defensive level.

Practice: Identify phishing attempts in educational exercises.

• AI and machine learning for spam, phishing, malware and fraud detection.
• Anomaly detection and Security Operations Centre applications.
• AI-assisted threat detection, false positives and false negatives.
• Human-in-the-loop security and the limitations of AI-based systems.

Practice: Explore AI-based spam and phishing detection.

• AI-enabled threats, AI-generated phishing and deepfakes.
• Prompt injection, safe prompting, data poisoning and adversarial inputs at a conceptual level.
• Privacy, bias, fairness, transparency, accountability and ethical AI use.
• Final practical activity and mini-project presentation.

Practice: Apply safe prompting, recognise deepfakes and present a mini-project or application-based activity.