Course Catalogue

Deep Learning (M) COMPSCI5085

  • Academic Session: 2026-27
  • School: School of Computing Science
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 2
  • Available to Visiting Students: Yes
  • Collaborative Online International Learning: No
  • Curriculum For Life: No

Short Description

This course is the next step beyond our introductory machine learning course and teaches students about modern techniques for machine learning with high-dimensional image and sequence (time-series) data, and the underlying computational structures for such systems.

Timetable

Three hours per week.

Requirements of Entry

Data Fundamentals (H)

Machine Learning (H) or Machine Learning for Data Scientists (M)

Excluded Courses

None

Co-requisites

None

Assessment

Examination 80%, Written Assignment 15%, Set Exercise 5%.

Main Assessment In: April/May

Course Aims

The aim of this course is to go beyond our introductory machine learning course, and teach students about modern techniques for machine learning with high-dimensional image and sequence (time-series) data, and the underlying computational structures for such systems. Teach the students about managing large data sets, and the engineering pipelines for large-scale machine learning tasks.  In this course, students will learn the foundations of deep learning and dynamic models for time-series analysis.

Intended Learning Outcomes of Course

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

1. Understand the major technology trends in advanced machine learning;

2. Build, train and apply fully connected deep neural networks;

3. Know how to implement efficient, vectorised neural networks in python and understand the underlying backends;

4. Apply deep learning methods to new applications;

5. Understand the machine learning pipeline, and engineering aspects of training data collation, and the importance of unlabelled data.

Minimum Requirement for Award of Credits

No exceptions