Overview of AI paradigm and applications of AI

Introduction to Cutting-edge AI services

Datasets and their properties including ethical considerations

Spatio and Temporal reasoning

Build and train Machine Learning model

Data Mining and the principles of Descriptive, Predictive and Prescriptive analytics

Data Acquisition and Quality Standards

Data pre-processing and statistical modelling

Data Segmentation

Data Transformation (Feature Extraction and Feature Selection)

Data cleaning and data labelling

Machine Learning

Anomaly Detection

Prediction

Supervised Techniques and Algorithms

Classification, regression

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

Unsupervised Techniques and Algorithms

Clustering, Association

K-means, DBSCAN, HBOS, XBO

Semi-supervised learning

Data Dimensionality reduction

ML performance evaluation

E.g. Recall, F-measure, Accuracy

An assignment comprising:



WRITTEN: Assessment 1 weighted at 50%: a classification review of ML techniques and DM approaches for the given task scenario (e.g. Digital Health monitoring, Crowd and traffic Management, Smart Homes). (Learning Outcomes 1 and 2)



PRACTICAL/TECHNICAL PRESENTATION: Assessment 2 weighted at 50%: Design, implement, test 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 their AI solution using the theories and practices covered in the indicative content. (Learning Outcome 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



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

Analysis, Reflection, Problem Solving



3. Build and train ML model/software, and presenting logical and coherent written arguments for choices made in relation to ML technique and DM approaches

Problem Solving, Reflection, Communication, Application

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.



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

ISBN:1447173066 9781447173069

Engelbrecht, A.P. (2007) Computational intelligence: an introduction. 2nd edn. Hoboken, N.J: John Wiley. ISBN:¿0470017333

Witten, I.H., Frank, E. and Hall, M. (2011)¿Data mining: practical machine learning tools and techniques.¿3rd edn. London; Amsterdam: Morgan Kaufmann. EAN: 9780080890364

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

EAN: 9781787126022



An annually updated keylinks online resource bank will be made available

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

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 problem. 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.