| Course Title | Machine Learning and Predictive Analytics |
| Course Code | M-233-04 |
| Level | Master |
| ECTS Credits | 6.00 |
| Faculty/unit | Engineering Faculty |
| Field | Computer and information sciences |
| Course Type | Standard |
| Course Leader | Pavlyuk Dmitry - Dr. sc. ing. professor |
| Annotation | This course will equip students with knowledge and understanding of tools and techniques commonly utilised within the field of Machine Learning. The course will set the context of the machine learning and predictive analytics utilisation in business intelligence. Courses discusses range of applications for predictive analytics. Wide range of the machine learning techniques will be considered in the course Decision tree learning, Artificial neural networks, Naive Bayes classifier, Genetic algorithms etc. |
| Aim | This course will provide students with knowledge and understanding of tools and techniques commonly utilised within the field of Machine Learning to solve complex problems. |
| LO1 | Synthesise evidence on the value of data as an asset for businesses to “mine” knowledge and “predict” trends |
| LO2 | Identify learning problems including classification, clustering and reinforcement; distinguish their scope and outline suitable solutions |
| LO3 | Develop and evaluate predictive analytics approaches and techniques such as regression and random forest classifiers |
| LO4 | Apply problem solving skills necessary for identifying the organisational capacity needed to employ a predictive analytics solution |
| LO5 | Visualise and present the results of predictive and descriptive models alongside an evaluation of performance and recommendations for improvement |
| LO6 | Understand predictive analytics trends and challenges and illustrate fluency with software tools used in predictive analytics |
| Required Literature | - Learning Analytics, Emerald Publishing Limited, 2019. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/tsilv/detail.action?docID=5877937.
- Dean, Jared, and Jared Dean. Big Data, Data Mining, and Machine Learning : Value Creation for Business Leaders and Practitioners, John Wiley & Sons, Incorporated, 2014. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/tsilv/detail.action?docID=1687540.
- Winters, Ralph. Practical Predictive Analytics, Packt Publishing, Limited, 2017. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/tsilv/detail.action?docID=4900799.
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| Assessment Method | This course is assessed through a single assessment. Students are required to produce a written report, using appropriate case studies and models to support their decisions. The report is targeted at solving a business-related problem based on given requirements and data, proposing a solution and preparing a pilot predictive model. This component brings together module material on the context, data and requirements for implementing a predictive module and in the course of completion students will gain experience in model building, presenting results and evaluating accuracy. |
| Independent study | During course additional materials will be provided for self-reading. The rest independent study will consist of preparation of the project element, which will be present as a report. |
| Full-time |
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| First Sit Elements | Element Weighting, % | Group Work | Links to Results |
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| Reports | 100 | | LO1, LO2, LO3, LO4, LO5, LO6 |
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