Machine Learning for Data Scientists (M) COMPSCI5090

  • Academic Session: 2019-20
  • School: School of Computing Science
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 1
  • Available to Visiting Students: No
  • Available to Erasmus Students: No

Short Description

A practical introduction to the foundations of machine learning, based around a series of case studies using data from a range of research areas. 


3 hours per week.

Requirements of Entry

For MSc students: acceptance into one of the MSc programmes listed in section 10 below.


Programming and Systems Development is an alternative entry requirement for this course.

Excluded Courses

Machine Learning (H)

Machine Learning (M)


Programming and Systems Development (H)

Introduction to Data Science and Systems (M)


Examination 50%, Coursework 50%.

Main Assessment In: December

Are reassessment opportunities available for all summative assessments? No

The coursework cannot be redone because the feedback provided to the students after the original coursework would give any students redoing the coursework an unfair advantage.

Course Aims

To present students with an introduction to the general theory of learning from data and to a number of popular Machine Learning methods. The module will be focussed on practical application of Machine Learning techniques from standard libraries such as scikit-learn in a variety of domains as well as good practice in training and validating Machine Learning models. The course will be based around weekly case studies using data from e.g. Human Computer Interaction, Information Retrieval, Bioinformatics and Computer Vision and Graphics.

Intended Learning Outcomes of Course

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

1. Explain the process of learning from data to problems in regression, classification and clustering;

2. Implement, customise and apply machine learning algorithms in Python using existing libraries (e.g. scikit-learn) to regression, classification, clustering, probability density estimation and dimensionality reduction;

3. Explain the strengths and weaknesses of a selection of common algorithms and propose appropriate choices for specific problems;

4. Discuss the impact of the choice of loss function, and propose suitable loss functions for specific problems;

5. Demonstrate knowledge of the major machine learning application areas in, for example Physics, Information Retrieval, Human Computer Interaction, Bioinformatics and Computer Vision & Graphics;

6. Demonstrate proficiency in the data-driven modelling process (Be able to setup a machine learning project, organise data and code, develop and apply a model, visualise and analyse the results, and communicate them in a coherent document).

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.