This module addresses the following topics:

Theory & Knowledge Exchange -

Overview of AI paradigm and applications of AI
Introduction to Cutting-edge AI services
Spatio and Temporal reasoning
Supervised Techniques and Algorithms
Classification, regression
Support Vector Machine (SVM), Decision Tree, and Artificial Neural Network (ANN)
Unsupervised Techniques and Algorithms
Clustering, Association
K-means, DBSCAN, HBOS, and XBOS
Semi-supervised learning
Data Dimensionality reduction

Practical Content -
Datasets and their properties
Data Mining
Data Acquisition
Data pre-processing
Data Segmentation
Data Transformation (Feature Extraction and Feature Selection)
Data cleaning and data labelling
Machine Learning
Anomaly Detection
Prediction
ML performance evaluation, e.g., Recall, F-measure, and Accuracy

WRITTEN REPORT: A report that provides a classification review of ML techniques and DM approaches for a given task scenario (e.g., Digital Health monitoring, Crowd and traffic Management, or Smart Homes) (Learning Outcomes 1 and 2).

PRACTICAL DEMONSTRATION: A practical assessment that designs, implements, tests, and documents an AI software. Students will be expected to analyse a given task scenario to define the business problem, and they need to design, develop, and test an AI solution using the theories and practices covered in the module (Learning Outcomes 3 and 4).

All teaching sessions will blend theory and practical learning. Students will be introduced to curriculum concepts and ideas and will then be able to apply theory to practical examples within the same sessions. In addition, students will be provided with a range of resources for independent study such as case studies, academic papers and industry stories. There will be a mixture of practical and theoretical formative (mock or practice) exercises which will help students build knowledge and confidence in preparation for summative (formal) assessment.

1. Demonstrate a critical understanding of Artificial Intelligence (AI) applications, Machine Learning (ML) techniques and Data Mining (DM) approaches through real world examples.

Knowledge and Understanding, Learning

2. Analyse a given task scenario, define the business problem and select an appropriate ML technique and DM approach to implement an AI solution to a given system task and critically appraise the solution.

Analysis, Reflection, Problem Solving

3. Implement AI solutions/software, and present logical and coherent written arguments for choices made in relation to ML techniques and DM approaches.

Problem Solving, Reflection, Communication, Application

4. Critically evaluate and compare the possible solutions of a complex problem.

Problem Solving, Reflection

JetBrains PyCharm (IDE for Python)
Numpy (Python library)
Pandas (Python library)
Scikit-learn API, or similar API
Datasets from Kaggle, Weka, MIMIC, or similar sources

All texts and electronic resources will be updated and refreshed on an annual basis and available for students via the online Study Links resource platform. All reference materials will be collated and curated and aligned to Equality, Diversity & Inclusion indicators.

Core Text/Resource:
Neil, C. (2020), Artificial Intelligence: 4 books in 1: AI for Beginners + AI for Business + Machine Learning for Beginners + Machine Learning and Artificial Intelligence, Independently published, ASIN: B086PVL7CB
Geron, A, (2019), Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, OReilly; 2nd New edition, ISBN-10: 1492032646
Campbell, C, (2020), PYTHON PROGRAMMING: 3 BOOKS IN 1: The Complete guide to Learn Everything you Need to Know about Python, ISBN: 180154767X

Optional Text/Resource:
Gupta, S, & Tu, P, (2020), What Is Artificial Intelligence? A Conversation Between an Ai Engineer and A Humanities Researcher, World Scientific Europe Ltd, ISBN-10: 1786348632
Raschka, S. (2017) Python machine learning: machine learning and deep learning with Python, scikit-learn, and TensorFlow. EAN: 9781787126022

All resources will be updated regularly and available via a module KeyLinks online function.