Machine Learning for Data Science BUS5065

  • Academic Session: 2023-24
  • School: Adam Smith Business School
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No

Short Description

Machine learning is a rapidly evolving field that involves developing algorithms capable of learning, adapting to new data, and providing valuable insights, predictions, and representations of the data. The course is designed to equip students with the knowledge and skills necessary to make informed decisions and develop strategies using machine learning tools. It is ideal for those seeking a comprehensive understanding of this field.

Timetable

The course will be delivered on a blended mode that includes active learning workshops over 6 sessions.

Requirements of Entry

Please refer to the current postgraduate prospectus and https://www.gla.ac.uk/postgraduate/.

Excluded Courses

None

Co-requisites

None

Assessment

The course will be assessed by

ILO

Assessment

Weighting

1, 2, 3 and 4

 

Individual report of max 3,000 words, where students collect data to critically analyse machine learning methods 

100%

.

Course Aims

This course aims to provide students with a strong foundation in both the theoretical concepts and practical techniques (methods and algorithms) of machine learning in data science. By providing a comprehensive understanding of these cutting-edge tools, students will be empowered to apply them to complex problems in changing environments.

Intended Learning Outcomes of Course

By the end of this course students will be able to:

1. Formulate an applied problem as a machine learning task and select appropriate methods to address specific economic issues.

2. Evaluate and validate different machine learning methods for a given task and provide constructive criticism.

3. Develop refine implementations of machine learning algorithms and apply them effectively in practical situations.

4. Analyse and interpret insights extracted from machine learning methods and the data utilized.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.