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2024/25 Undergraduate Module Catalogue

ELEC1301 Computational Foundations of Artificial Intelligence

20 creditsClass Size: 180

Module manager: Dr Aleksandar Demic
Email: a.demic@leeds.ac.uk

Taught: Semesters 1 & 2 (Sep to Jun) View Timetable

Year running 2024/25

Pre-requisite qualifications

Acceptance onto the BEng/MEng Electronics and Computer Engineering, or BEng/MEng Mechatronics and Robotics Engineering programme

This module is not approved as a discovery module

Module summary

This module provides an introduction to core aspects of computing essential for modern engineering and applied artificial intelligence practice. It is intended to give students a good grasp of a contemporary programming language and principles in algorithms and data structures, which are essential to designing, implementing and evaluating software and algorithms in problem solving.​

Objectives

The module has the following objectives:
- To provide a hands-on approach to the study of core topics in computing for modern engineering and applied artificial intelligence practice.
- To develop an understanding of the implementation of different data structures and algorithms with Python
- To learn how to apply different data structures and algorithms with Python
- To analyse the performance of different data structures and algorithms in different problem-solving contexts.

Learning outcomes
On successful completion of the module students will have demonstrated the following learning outcomes:
1. Apply basic knowledge of mathematics, statistics, and engineering principles to the solution of well-defined computing problems.
2. Analyse well-defined computing problems to reach substantiated conclusions using first principles of mathematics, statistics, and engineering principles.
3. Apply appropriate computational and analytical techniques to model well-defined computing problems.

Skills learning outcomes

On successful completion of the module students will have demonstrated the following skills:
a) Application of science, mathematics and/or engineering principles
b) Problem analysis
c) Application of computational and analytical techniques


Syllabus

* Fundamental of Software Design
* Introduction to Programming with Python​
* Introduction to Data Analysis using Python
* Fundamentals of Algorithms​
* Data Structure and Algorithms​
* Fundamentals of Graphs and associated Algorithms
* Time series data processing on MCUs​
* Application of Algorithms such as sorting, hashing etc.
* Performance Analysis of Algorithms (complexity, energy consumption etc.)​
* Graph and Optimisation algorithms
* Case-studies applying the learnt knowledge

Methods of Assessment

We are currently refreshing our modules to make sure students have the best possible experience. Full assessment details for this module are not available before the start of the academic year, at which time details of the assessment(s) will be provided.

Assessment for this module will consist of:

1 x Coursework
2 x Exam

Teaching methods

Delivery typeNumberLength hoursStudent hours
Example Class81.008.00
Consultation161.0016.00
Lectures41.004.00
Practicals162.0032.00
Independent online learning hours30.00
Private study hours110.00
Total Contact hours60.00
Total hours (100hr per 10 credits)200.00

Opportunities for Formative Feedback

Students studying ELEC modules will receive formative feedback in a variety of ways, which may include the use of self-test quizzes on Minerva, practice questions/worked examples and (where appropriate) through verbal interaction with teaching staff and/or post-graduate demonstrators.

Methods of assessment


Coursework
Assessment typeNotes% of formal assessment
In-course AssessmentCoursework40.00
In-course AssessmentClass Test 130.00
In-course AssessmentClass Test 230.00
Total percentage (Assessment Coursework)100.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

There is no reading list for this module

Last updated: 29/04/2024 16:13:30

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