CS5938 Numeric Optimisation

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

Professor Tom Kelsey and Professor Stephen Linton

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

Module description

An ubiquitous component of Data Science is the minimisation of carefully-chosen error functions, but many practitioners and researchers employ the standard software package defaults without really understanding the underlying problem and how it is being solved. This module takes linear algebra and optimization as the primary topics of interest, and solutions to machine learning problems as the application of the resulting tools, techniques and algorithms. For CS5938 the types of optimisation include gradient descent (and variants); constrained optimisation and duality; singular value decomposition; and the optimisation of computational graphs.

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

  • Understand key concepts & techniques in numerical analysis and linera algebra
  • Apply the concepts to Data Science workflows and be able to critically evaluate competing approaches
  • Be able to implement and deploy variants of the optimization methods for Deep Learning
  • Write python code to solve linear, quadratic and global optimisation problems.

CS5938 Numeric Optimisation (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

Professor Tom Kelsey and Professor Stephen Linton

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

Module description

An ubiquitous component of Data Science is the minimisation of carefully-chosen error functions, but many practitioners and researchers employ the standard software package defaults without really understanding the underlying problem and how it is being solved. This module takes linear algebra and optimization as the primary topics of interest, and solutions to machine learning problems as the application of the resulting tools, techniques and algorithms. For CS5938 the types of optimisation include gradient descent (and variants); constrained optimisation and duality; singular value decomposition; and the optimisation of computational graphs.

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

  • Understand key concepts & techniques in numerical analysis and linera algebra
  • Apply the concepts to Data Science workflows and be able to critically evaluate competing approaches
  • Be able to implement and deploy variants of the optimization methods for Deep Learning
  • Write python code to solve linear, quadratic and global optimisation problems.