CS5914 Machine Learning Algorithms

Academic year

2024 to 2025 Full Year

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

15

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 11

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Availability restrictions

Available only to students studying the PG Cert/PG Dip/MSc in Data Science (Digital)

Module coordinator

Prof T W Kelsey

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module Staff

Dr Lei Fang

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

Machine Learning enables computers to improve automatically with experience. A growing number of algorithms are being used to predict outcomes using patterns in collected data. This module covers the essential theory and algorithms, including mathematical foundations, and methodological approaches. It covers a variety of regression, classification and unsupervised approaches.

Assessment pattern

Coursework = 100%

Re-assessment

Coursework = 100%

Learning and teaching methods and delivery

Weekly contact

Students should expect to engage in approximately six tutorials over the course of the module, which will be scheduled with an awareness of the pace at which they are progressing, rather than at a fixed time each week. Students should consider the amount of independent study time this module involves when planning their learning.

Scheduled learning hours

6

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

148

The number of hours that students are expected to invest in independent study over the period of the module.

Intended learning outcomes

  • be able to demonstrate the main concepts in machine learning
  • demonstrate knowledge of important algorithms in the field and when to use them
  • be able to apply machine learning to solve practical problems
  • understand how to optimise algorithms for specific tasks

CS5914 Machine Learning Algorithms (15 credits)

Academic year

2024 to 2025 Flexible calendric study (eg, Terrorism Studies)

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

0

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 11

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Availability restrictions

Available only to students studying the PG Cert/PG Dip/MSc in Data Science (Digital)

Module coordinator

Prof T W Kelsey

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module Staff

Dr Lei Fang

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

Machine Learning enables computers to improve automatically with experience. A growing number of algorithms are being used to predict outcomes using patterns in collected data. This module covers the essential theory and algorithms, including mathematical foundations, and methodological approaches. It covers a variety of regression, classification and unsupervised approaches.

Assessment pattern

Coursework = 100%

Re-assessment

Coursework = 100%

Learning and teaching methods and delivery

Weekly contact

Students should expect to engage in approximately six tutorials over the course of the module, which will be scheduled with an awareness of the pace at which they are progressing, rather than at a fixed time each week. Students should consider the amount of independent study time this module involves when planning their learning.

Scheduled learning hours

6

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

148

The number of hours that students are expected to invest in independent study over the period of the module.

Intended learning outcomes

  • be able to demonstrate the main concepts in machine learning
  • demonstrate knowledge of important algorithms in the field and when to use them
  • be able to apply machine learning to solve practical problems
  • understand how to optimise algorithms for specific tasks