Course Catalogue

Deep Learning UESTCHN5007

  • Academic Session: 2026-27
  • School: School of Engineering
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 1
  • Available to Visiting Students: No
  • 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

N/A

Excluded Courses

None

Co-requisites

None

Assessment

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

Main Assessment In: December

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