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DRPS : Course Catalogue : Business School : Common Courses (Management School)

Postgraduate Course: Applied Machine Learning (CMSE11614)

Course Outline
SchoolBusiness School CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate)
Course typeOnline Distance Learning AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryThis course provides students with the fundamentals of supervised and unsupervised learning models to analyse real-world business applications.
Course description This course aims at training students in the field of machine learning to respond to the job market needs using a variety of supervised and unsupervised learning methodologies.

The course covers the typical methodological steps of a prediction exercise, statistical modelling, and artificial intelligence methodologies for prediction of applications in business and economics. It also covers practical issues in predictive analytics and how to address them.

The objective of this course is to enhance students' understanding of the importance of adopting a series of sound methodological steps in a prediction exercise and to provide them with a toolkit of modelling and prediction techniques along with hands-on experience in using them.

Course outine content

- Unsupervised Learning and Preparation of Data

- Introduction to Linear Regression and Logistic Regression

- Model Tuning, Bias-Variance Trade-off, Data Splitting, Resampling Techniques, Addressing Imbalance

- Linear Model Selection and Regularization

- Multiple logistic regression, dimensionality reduction, PCA, PCR

- Tree-based models

- Neural networks and deep learning

Student learning experience

Tutorial/seminar hours represent the minimum total live hours - online - a student can expect to receive on this course. These hours may be delivered in tutorial/seminar, workshop or other interactive whole class or small group format. These live hours may be supplemented by pre-recorded lecture material for students to engage with asynchronously. Live sessions will be delivered only once.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2023/24, Not available to visiting students (SS1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 10, Seminar/Tutorial Hours 20, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 166 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 70% Project report (Individual) - Assesses all course Learning Outcomes
30% Pitch (Individual) - Assesses all course Learning Outcomes
Feedback Formative: Delivered throughout live sessions (Q&A).

Summative: Feedback is provided on asssessment.

No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Critically understand and discuss machine learning concepts and methods with particular focus on supervised and unsupervised learning techniques.
  2. Identify and properly state research problems related to prediction analytics in different business settings.
  3. Critically discuss alternative prediction approaches and methods, and choose the right prediction models for a prediction exercise, implement them, and prepare predictions.
  4. Interpret solutions, formulate managerial guidelines and make recommendations to a critical audience of specialists and non-specialists.
Reading List
Kuhn, Max, and Kjell Johnson. Applied predictive modelling. New York: Springer, 2013.

James, G., Witten, D., Hastie, T., & Tibshirani, R. An introduction to statistical learning. New York: Springer, 2021.
Additional Information
Graduate Attributes and Skills Communication, ICT, and Numeracy Skills

After completing this course, students should be able to:

Critically evaluate and present digital and other sources, research methods, data and information; discern
their limitations, accuracy, validity, reliability and suitability; and apply responsibly in a wide variety of
organisational contexts.

Practice: Applied Knowledge, Skills and Understanding

After completing this course, students should be able to:

Apply creative, innovative, entrepreneurial, sustainable and responsible business solutions to address
social, economic and environmental global challenges.

Cognitive Skills

After completing this course, students should be able to:

Be self-motivated; curious; show initiative; set, achieve and surpass goals; as well as demonstrating
adaptability, capable of handling complexity and ambiguity, with a willingness to learn; as well as being able to
demonstrate the use digital and other tools to carry out tasks effectively, productively, and with attention to

Knowledge and Understanding

After completing this course, students should be able to:

Demonstrate a thorough knowledge and understanding of contemporary organisational disciplines;
comprehend the role of business within the contemporary world; and critically evaluate and synthesise primary
and secondary research and sources of evidence in order to make, and present, well informed and transparent
organisation-related decisions, which have a positive global impact.

Identify, define and analyse theoretical and applied business and management problems, and develop
approaches, informed by an understanding of appropriate quantitative and/or qualitative techniques, to explore
and solve them responsibly.
KeywordsNot entered
Course organiserDr Belén Martín-Barragán
Tel: (0131 6)51 5539
Course secretaryMs Heather Ferguson
Tel: (0131 6)50 8074
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