This module will cover topics of:



Machine Learning

Anomaly Detection

Prediction

Supervised Techniques and Algorithms

Classification, and regression

Support Vector Machine (SVM), Decision Tree, and Artificial Neural Network (ANN)

Unsupervised Techniques and Algorithms

Clustering, Association

K-means, DBSCAN, HBOS, XBOS

Reinforcement Learning

Evolutionary Algorithm

Data Dimensionality reduction

ML performance evaluation

E.g. Recall, F-measure, and Accuracy

Group Presentation - A group presentation of classification and review of ML techniques and DM approaches for a given task scenario (e.g. Digital Health Monitoring, Crowd and Traffic Management, of Smart Homes), (Learning Outcomes 1 and 4)

Practical Assessment - A practical assignment that leads to hands on experience of using AI-purposed libraries and tools as well as coding of own algorithms. Students will design, implement, test and document an AI software solution. Students will be expected to analyse a given task scenario (e.g. Digital Health Monitoring, Crowd and Traffic Management, or Smart Homes) and to define the business problem that they need to design for, and develop and test their AI solution using the theories and practices covered in the module. This work will be assessed by demonstration (Learning Outcomes 1, 2 and 3)

Theory will be delivered via lectures and supported by practical classes, seminars and discussion groups. In addition, you 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 exercises which will help you build your knowledge and confidence as well as preparing you for the summative 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, Enquiry

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 a ML model, and presenting logical and coherent written arguments for choices made in relation to ML technique and DM approaches

Problem Solving, Reflection, Application

4. Critically evaluate and compare the possible solutions of the investigated 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

These are indicative only. Texts are updated on an annual basis and when you start to study this module, you will be referred to an online reading list, currently provided through Keylinks. You are advised not to buy any textbooks for this module without checking the online reading list.



Theobald, A. (2021), Machine Learning for Absolute Beginners: A Plain English Introduction (Third Edition): 1 (Machine Learning with Python for Beginners), Independently published

Geron, A. (2022), Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3e: Concepts, Tools, and Techniques to Build Intelligent Systems, O'Reilly Media; 3rd edition

Huyen, C. (2022), Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, O'Reilly Media; 1st edition

Tombs, N. (2022), Reflections of a Robot: Dialogues on Artificial Intelligence, Independently Published

Bramer, M.A. (2016)¿Principles of data mining.¿3rd edn. London: Springer.

Engelbrecht, A.P. (2007) Computational intelligence: an introduction. 2nd edn. Hoboken, N.J: John Wiley.

Witten, I.H., Frank, E. and Hall, M. (2011)¿Data mining: practical machine learning tools and techniques.¿3rd edn. London; Amsterdam: Morgan Kaufmann.

Raschka, S. (2017) Python machine learning: machine learning and deep learning with Python, scikit-learn, and TensorFlow.

Artificial intelligence (AI) is one of the oldest fields of computer science that strives to understand the essence of intelligence to compose a new intelligent machine that responds like human intelligence. In recent years, AI in modern business has not only made operations faster and more efficient but has also produced increasingly competitive products. This module establishes a solid understanding of the characteristics and implications which are inherent in the solution of complex, real-world data science and AI-oriented problems. As a student on this module, you will learn to critically evaluate and apply different ML and DM techniques for exemplifying data patterns, as well as using professional ML tools to build your AI software solution.