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IS41070

Academic Year 2024/2025

Machine Learning Foundations (IS41070)

Subject:
Information Studies
College:
Social Sciences & Law
School:
Information & Comms Studies
Level:
4 (Masters)
Credits:
15
Module Coordinator:
Dr Arjumand Younus
Trimester:
Summer
Mode of Delivery:
Blended
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

Machine learning (ML) is a category of algorithms that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. Machine Learning is used anywhere from automating mundane tasks to offering intelligent insights, and is consequently becoming prevalent across all industries.

This module is designed to provide an understanding of ML and how it relates to modern data analysis. Furthermore, students may expect to gain knowledge of the practical application of the core components of ML in terms of domain (data) applicability, classifier types, and the ongoing challenges in terms of the adoption of ML approaches.

About this Module

Learning Outcomes:

On successful completion of this module the student will be able to:

1. Understand the principles and the purposes of machine learning.
2. Identify problems which are suitable for the application of machine learning.
3. Retrieve and analyse real-world datasets.
4. Use appropriate machine learning techniques for a given data analytics problem.
5. Apply the process of data understanding and address data quality issues.
6. Design evaluation experiments for selecting the best predictive model for a given problem.

Indicative Module Content:

Python programming, data analysis, scientific method

Student Effort Hours:
Student Effort Type Hours
Autonomous Student Learning

310
Practical

60
Total

370

Approaches to Teaching and Learning:
Lectures and lab work: workshop styled delivery with practical components facilitated by tutors.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy  
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered

Not yet recorded.

Carry forward of passed components
No
 

Resit In Terminal Exam
Autumn No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Group/class feedback, post-assessment
• Online automated feedback

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Agatha Carolina Hennigen de Mattos Lecturer / Co-Lecturer
Dr Arjumand Younus Lecturer / Co-Lecturer
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