The aim of this module is to develop the learners understanding and appreciation of emerging and future technologies in Artificial Intelligence and Machine Learning. Learners will be required to investigate novel applications, research areas and environments where Artificial Intelligence and Machine Learning can be beneficial; to consider the ethical, legal, social, economic, environmental, and cultural issues on which Artificial Intelligence and Machine Learning may have influence and be influenced by.
The most current, up-to-date and emerging research areas in Artificial Intelligence and Machine Learning (including aspects of wider technology) will be considered in this module. Naturally, these fields will change rapidly and will need to be reviewed on a regular basis. However, at the time of writing, indicative syllabus content areas could include:
Week 1: Appreciation of past and present approaches to Artificial Intelligence and Machine Learning
Early Computers and the Birth of AI, Knowledge as Rules, Top-down v’s Bottom-up AI, AI as a Search Problem, P Vs NP Complete, Big Data, the Resurgence of Neural Networks and Deep Learning.
Week 2,3: Open Problems and Challenges
Explainable Machine Learning (XAI), Inclusivity, Privacy, Trust, Causality, Normativity, Model Drift, Generalisability and Artificial General Intelligence (AGI).
Week 4: Societal and Economic Impact
Industry 4.0, AI for all – Economic Gaps, Labour Automation, Product and Service Innovation, Mobility, Augmented Health-care and Diagnosis.
Week 5: Environmental Impact
Carbon Footprint of AI and Machine Learning, Measuring and Reporting Carbon Cost, Federated Learning, Edge-Based Computing and AI, Model Compression, Auto-ML and Environmentally Friendly AI.
Week 6,7: Advances in Machine Learning Models through a Human Centred Lens
Semi-supervised and Unsupervised learning , Generative Models, Transform Deep Learning, Federated Learning, Hybrid learning models, Model compression, Hyperparameter tuning and Auto-ML
Week 8,9, 10: Emerging Solutions for Human Centred Artificial Intelligence and Machine Learning
Post-hoc and Causal Explainability, Feature Importance, Trust Models and Trust quantification, Probabalistic descriptions of ML models, Subjective logic, Permutation Importance, Partial Dependence, Individual Conditional Expectation (ICE), Local Interpretable Model-agnostic Explanations (LIME), Deep Learning Importance Features (DeepLIFT), Shapley Additive Explanations (SHAP).
Week 11, 12: Philosophical and Discussion Topics for Future Artificial Intelligence and Machine Learning
Permeation of AI, The AI Singularity, Neuromorphic Technologies, Human-machine Biology, Quantum Computing, Living with Robots.
This module will be presented over 12 lectures, and copies of the lecture material will be provided. Students will be expected to use library and internet based information sources extensively, and familiarise themselves with the supplemental reading. Students will be encouraged to be proactive in their approach to learning for this module which will require independent research and critical thinking. The continuous assessment will take the form of case studies, reports and presentations. Students will be required to work both independently and as part of groups. Where possible real-world examples will be used.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Other Assessment(s) | 100 |