The Health would include Medical, Bio-medical and clinical studies;  Wealth would include economics and business applications; Happiness would include psychology and well-being.   Society would probably have to be confined to news (one year in arrears?), but include Millennium Goals Development, and Climate Change veering away from Politics. 

For the first year I would suggest we use Excel for data management and SPSS for analysis (introducing R in the second year). 

For the first year I would aim for two assessment points. 

One assessment point being a six-item engagement portfolio over the whole year (each item with categorical marking e.g. 42, 45, 48, 52, 55, 58, 62, 65, 68, 72, 75, 78, 82, 85, 88 etc or similar) and one coursework designed so the student can showcase their understanding and their developed skillset including communication. 

In the first year we would aim to show the differences between statistics for scientific reasoning, statistics for decision making, and statistics for prediction and classification. 

For the second year I would suggest the module progresses in a similar manner but reaching out to the Programming Stream as we could include some in-silico simulation for problem solving within statistics and to demonstrate statistical logic (e.g. sampling distributions, bootstrapping) and provide an Intro to ML. 

At the end of each year, there would be a reflective session on the skills developed, and knowledge accrued, and how this fits in with employability. 

There is some resistance to the above e.g. not wanting to create a lot of new materials. 

1. Deep Learning: Understanding the architecture and applications of various deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers.

2. Reinforcement Learning: An introduction to the concepts of reinforcement learning, exploring algorithms like Q-Learning and policy gradients.

3. Natural Language Processing (NLP): Covering techniques for processing and understanding human language, including word embeddings, sequence-to-sequence models, and attention mechanisms.

4. Generative Models: Learning about models that can generate new data, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).

5. Explainability and Fairness in AI: Discussing the importance of interpretability in machine learning models, techniques for achieving it, and the concept of fairness in AI.

6. Advanced Topics: Covering recent trends and advancements in machine learning, such as self-supervised learning, few-shot learning, and large language models.

Assessment Idea:

A project in which students find a dataset of their choice and apply one or more of the above techniques. Explainability and fairness could be required in all cases.

This can be seen here: (https://uwe.worktribe.com/record.jx?recordid=11885367).

This module is inspired by Statistical Investigation 2023-24 (https://uwe.worktribe.com/record.jx?recordid=7772267).

Year 2: Applied Statistics and Fundamentals of Machine Learning (30)

This can be seen here: (https://uwe.worktribe.com/record.jx?recordid=11885381).

This module is inspired by Statistical Applications 2023-24 (https://uwe.worktribe.com/record.jx?recordid=6790813).

Year 3:  Advanced Machine Learning and Statistics (30)

This can be seen here: (https://uwe.worktribe.com/record.jx?recordid=11885386).

This module is inspired by Clustering And Classification 2023-24 (https://uwe.worktribe.com/record.jx?recordid=6790663) and Statistical Practice 2023-24 (https://uwe.worktribe.com/record.jx?recordid=6790794)