Course Catalogue

Artifiicial Intelligence and Machine Learning for Communications and Signal Processing UESTC5001

  • Academic Session: 2026-27
  • School: School of Engineering
  • Credits: 20
  • 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 teaches students about modern Artificial Intelligence techniques to address complex engineering problems in communications and signal processing. The course introduces modern techniques in artificial intelligence, including deep learning and provides an overview of advanced programming libraries, including best practices in training and evaluating AI system.

Timetable

40 hours lectures

16 hours practical classes and workshops

Requirements of Entry

None

Excluded Courses

None

Co-requisites

None

Assessment

Individual assessments:

- one quiz delivered online through Moodle on the theoretical knowledge acquired by the students,10%

 - three coding exercises submitted online through Moodle, two worth 15%, one worth 10%

 - Individual report (20%) on strategy for solving a AI problem (approach to coding and to benchmarking the result) Group assessment:

 - development, implementation, training and benchmarking AI algorithm for problem: presentation and practical demonstration (30%)

Course Aims

This course aims to present students with modern techniques in artificial intelligence, including deep learning and reinforcement learning, to address large multidimensional datasets and complex data.

The course will evaluate AI in the context of sustainability and responsible innovation. Specific aspects related to energy demand, computational cost and infrastructure impact of AI will be presented and discussed.

The course focusses on the application of AI techniques to communication engineering and signal processing. The students will be introduced to the use of advanced libraries as well as providing good practice in training and validating models.

The course will include practical AI case studies.

Intended Learning Outcomes of Course

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

■ assess the impact of AI on our society, in terms of resource consumption and long-term sustainability of the AI based solutions;

■ design, build, train and apply fully connected deep neural networks to address specific tasks;

■ train agents to interact with a realistic environment using reinforcement learning algorithms;

■ judge the strengths and weaknesses of various AI/ML algorithms and propose appropriate choices for specific problems;

■ evaluate a complex engineering application and create a suitable AI-driven solution to address it.

Minimum Requirement for Award of Credits

No exceptions