Study-Unit Description

Study-Unit Description


CODE CIS2350

 
TITLE Business Applications of AI

 
UM LEVEL 02 - Years 2, 3 in Modular Undergraduate Course

 
MQF LEVEL 5

 
ECTS CREDITS 5

 
DEPARTMENT Computer Information Systems

 
DESCRIPTION Artificial Intelligence (AI) is currently experiencing a resurgence, called the 'AI Boom', that is driven, in part, by recent advances such as Deep learning. In the last few years the business benefits of AI have become apparent. Businesses have started to realise that leveraging artificial intelligence and machine learning, as a business tool, is critical for success. AI is today being used to obliterate inefficiency, optimize operations, save money, and maximize profits.

AI is revolutionizing areas such as customer care, IT security, finance, accounting, auditing, HR, marketing, sales, KYC, AML, and manufacturing. AI is making businesses smarter, better, and more efficient in the process. AI promises to radically change the way we live and conduct business.

This study-unit introduces the student to the techniques and best practices used to apply AI to business and industry. Students are expected to develop a broad understanding of recent AI developments and their impact on the business. Techniques covered include neural networks, deep learning, decision trees, PCA, image processing, NLP, information retrieval, and genetic algorithms. Students will also have hands-on and problem solving experiences that can be useful in AI applications and innovation. Python, including AI, NLP, and machine learning packages will be used to implement a number of use cases of AI applied to business problems.

Study-Unit Aims:
The aims of the study-unit are to:

- Introduce the best practices, standards, techniques, and concepts for applying AI for the automation of business and industry processes;
- Use case studies to clean obtained data and use best practices to share the data;
- Differentiate between the different AI and ML algorithms that can be applied to a business or industry problem;
- Evaluate which is the best AI or Machine Learning algorithm to apply depending on the type of data available and on the nature of the problem;
- Apply visualization best practices to present interesting conclusions drawn from the data.

Learning Outcomes:

1. Knowledge & Understanding:
By the end of the study-unit the student will be able to:

- Describe the AI and machine learning technologies used to solve or approximate, difficult problems in business and industry;
- Design and implement AI and ML applications in business and industry;
- Apply AI and Machine Learning principles, and assess their impact, on traditional business and industry processes.

2. Skills:
By the end of the study-unit the student will be able to:

- Discuss and critically analyse outcomes from use-cases covering current open problems in the field;
- Search for state of the art techniques to solve domain specific problems;
- Present a solution using proper visualisation tools;
- Analytical skills: Develop analytical skills by applying AI and machine learning techniques to a dataset;
- Assessment Skills: Use industry standard techniques to evaluate the performance of the algorithm chosen;
- Communications skills: Use techniques to share data with colleagues and present findings to a non-technical audience effectively.

Main Text/s and any supplementary readings:

Main Texts:

- Finlay, Steven. Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies. Relativistic. Third Edition, May 2017. ISBN-13: 978-1-9997303-6-9.
- Joshi, Prateek. Artificial Intelligence with Python: A Comprehensive Guide to Building Intelligent Apps for Python Beginners and Developers. Packt Publishing. January 2017. ISBN 978-1-78646-439-2.
- Liu, Yuxi (Hayden). Python Machine Learning By Example: The easiest way to get into machine learning (Kindle Locations 25-26). Packt Publishing. May 2017. ISBN 978-1-78355-311-2.

 
STUDY-UNIT TYPE Lecture, Independent Study & Tutorial

 
METHOD OF ASSESSMENT
Assessment Component/s Assessment Due Sept. Asst Session Weighting
Project SEM1 Yes 40%
Examination (2 Hours) SEM1 Yes 60%

 
LECTURER/S John M. Abela
Kristian Guillaumier
Dylan Seychell

 

 
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It should be noted that all the information in the description above applies to study-units available during the academic year 2024/5. It may be subject to change in subsequent years.

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