CS5959 End-to-End Machine Learning

Academic year

2024 to 2025 Flexible study

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), PG Cert/PG Dip/MLitt Digital Humanities, or PG Cert/PG Dip/MSc Digital Art History

Module coordinator

Dr B Varghese

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

Module Staff

TBC: Module coordinator(s): Computer Science (cs5959.staff@st-andrews.ac.uk)

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 workflows are key to effective Data Science. This module is focussed on using python packages to perform end-to-end data-driven analyses.

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

  • Determine what models are applicable for different data and objectives
  • Conduct hyperparameter-tuning/model-selection as appropriate to the model
  • Manipulate data, fit models, and summarise/display their results/performance and objectively compare models
  • Conduct comprehensive analysis of large real-world data covering: data preparation; model fitting, critique & refinement; and presentation of results to a range of audiences

CS5959 End-to-End Machine Learning (15 credits)

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), PG Cert/PG Dip/MLitt Digital Humanities, or PG Cert/PG Dip/MSc Digital Art History

Module coordinator

Dr B Varghese

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

Module Staff

TBC: Module coordinator(s): Computer Science (cs5959.staff@st-andrews.ac.uk)

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 workflows are key to effective Data Science. This module is focussed on using python packages to perform end-to-end data-driven analyses.

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

  • Determine what models are applicable for different data and objectives
  • Conduct hyperparameter-tuning/model-selection as appropriate to the model
  • Manipulate data, fit models, and summarise/display their results/performance and objectively compare models
  • Conduct comprehensive analysis of large real-world data covering: data preparation; model fitting, critique & refinement; and presentation of results to a range of audiences