This module addresses the following topics:

Case study based learning and simulation activities

Evolution of modern AI: symbolic, statistical, deep learning, generative AI

Artificial Intelligence (AI) vs Machine Learning (ML) vs Deep Learning (DL)

Types of learning: Supervised, unsupervised, reinforcement learning

Model evaluation: Accuracy, precision, recall, F1, ROC

Overfitting, bias-variance trade-off

Core algorithms: Regression, classification, clustering

Neural networks: CNNs, RNNs, LSTMs

Training processes: Optimisation (gradient descent, backpropagation)

Model tuning and validation

Introduction to frameworks: Python, TensorFlow / PyTorch

Transformers and attention mechanisms

Large Language Models (LLMs)

Prompt engineering and fine-tuning

Generative AI: Text, image, code generation

Limitations: Hallucinations, bias, reliability

Bias, fairness, and accountability

Explainable AI (XAI)

Privacy, GDPR, and governance

Environmental impact of AI systems

Sustainability factors associated to the AI discipline

Business enterprise within AI models, professional practice and entrepreneurship opportunities

Societal implications (accessibility and inclusion principles)

Supervised Learning for Threat Detection: Classification algorithms (Logistic Regression, Decision Trees, SVMs).

Applications: malware detection, phishing detection

Advanced Supervised Models and Evaluation: Ensemble methods (Random Forests), Evaluation metrics (precision, recall, F1-score, ROC), Handling imbalanced datasets

Feature Engineering for Security Data: Feature extraction from logs and network traffic, Feature selection techniques, Domain-specific feature design

Unsupervised Learning and Anomaly Detection: Clustering (K-means, DBSCAN), Detecting zero-day and unknown attacks, User and network behaviour analytics

Dimensionality Reduction and Data Visualization: Principal Component Analysis (PCA), Visualizing high-dimensional data. Improving efficiency and performance

Deep Learning for Cyber Security: Neural networks fundamentals, Malware and intrusion detection applications, Automated feature learning

Sequential and Temporal Models: RNNs and LSTMs, Time-series analysis in cyber security, Detecting attack patterns over time

Adversarial Machine Learning: Evasion and poisoning attacks, Model vulnerabilities, Defensive strategies

Big Data and Scalable Security Analytics: Handling large-scale security data, Distributed processing (e.g., batch vs streaming), Real-time threat detection systems, Introduction to platforms like Spark for security analytics

Reinforcement Learning: Adaptive and autonomous defence systems, Reinforcement learning basics

BCS / TechSkills / Employability elements:

System modelling is addressed within the module looking at requirements, commercial and business aspects.

Systems are evaluated in order to establish a base requirement in order to redesign and fit AI solutions.

Legal, social, ethical and professional issues are directly addressed in relation to AI and its take up in the industry.

Problem solving is addressed via practical AI related case study work.

Concepts of sustainability are addressed in relation to case studies and finding effective AI solutions.

Societal implications are addressed with a specific focus on accessibility and inclusion principles related to the AI discipline.

Principles of AI and cyber security are addressed practically.

CLASS-TEST - A multiple-choice test with questions that explore concepts, tools, legal, social, ethical, sustainability, cyber concepts, and professional impacts related directly to a series of case studies. Some of the questions within the test will require additional worded answers to support selections made.

REPORT - A report that discusses design requirements, implementation, and testing of a small solution to a provided case study. The report needs to identify technical, commercial, and business issues associated. The report must also document core themes of the British Computer Society in relation to the evolving AI landscape, including: legal, social, ethical, professional implications, and societal impact of AI systems in relation to a team-based scenario. The report must also identify commercial and entrepreneurship opportunities within the domain, reflecting on personal skills need to tap into this.

Formative assessment opportunities will be provided throughout the module. In creating the report staff will review progress regularly and give feedback on a draft. Related to the class-test there will be pre-circulated sample questions and group feedback as to potentially good answers. There will also be a mock test so you can gauge your learning.

All taught sessions will combine theoretical knowledge with hands-on learning by making extensive use of simulations. You will learn about various concepts and ideas from the curriculum and then use that theory to work on practical examples during the same sessions. Moreover, you will have access to a variety of materials for self-study, including additional case studies, scholarly articles, and industry narratives. There will be a blend of hands-on and theoretical practice exercises designed to enhance your knowledge and boost self-confidence as you get ready for formal assessments.

1. Critically evaluate modern AI techniques (e.g. Machine Learning (ML), Deep Learning (DL) and generative AI), related to their application to complex real-world problems.

Knowledge & Understanding
Digital literacy

2. Design, develop and implement AI-based solutions using appropriate tools, frameworks and datasets based on commercial and business issues, also identifying personal development and entrepreneurship opportunities.

Application & Problem-Solving
Personal Development and Entrepreneurship

3. Conduct rigorous research and critically analyse the performance, limitations, cyber concepts, legal, social, ethical, professional implications, and societal impact of AI systems working within team-based scenarios.

Research Skills
Critical Reasoning & Collaboration

4. Communicate complex AI concepts, methodologies and findings effectively to professional and non-specialist audiences, reflecting on personal and professional development.

Communication
Reflection

Standard Personal Computer (PC)

Python (Anaconda environment)

TensorFlow / PyTorch

Jupyter Notebooks

Access to datasets (Kaggle, UCI, etc.)

Online learning material

VMWare Workstation v16 or later

Kali Linux

Parrot OS

Host Machine with at least 8GB RAM, i5 or later processor, 250GB SSD Storage

Babushkin, V. and Kravchenko, A. (2025), Machine Learning System Design: With End-to-End Example, Manning Publications.

Seyedeh Leili Mirtaheri, S. L. and Shahbazian, R. (2022), Machine Learning: Theory to applications, CRC Press

Smolyakov, V. (2025), Machine Learning Algorithms in Depth, Manning Publications

Clinton, D. (2024), The Complete Obsolete Guide to Generative AI, Manning Publications

Atkinson-Abutridy, J. (2025), Large language models: concepts, techniques and applications, Boca Raton, FL: CRC Press

Within this module, you will learn and practice with modern AI and ML concepts (e.g. look at the evolution of AI, differences and applications of Artificial Intelligence (AI) vs Machine Learning (ML) vs Deep Learning (DL), types of learning, core algorithms, Neural Network (NN) types, generative AI, limitations, privacy, legislation, cyber security and governance, and relevant AI tools and frameworks). This will give you a deep understanding of efficient, successful and sustainable AI approaches, demonstrating accountability and compliance to the existing legislations. Your learning will be practically focused through case studies and simulation scenarios.