Course Details in 2022/23 Session

Module Title	LM Algorithms for Data Science
School	Computer Science
Department	Computer Science
Module Code	06 37862
Module Lead	Miqing Li
Level	Masters Level
Credits	20
Semester	Semester 1
Pre-requisites
Co-requisites
Restrictions	None
Contact Hours	Guided independent study-200 hours Total: 200 hours
Exclusions
Description	Modern data science encompasses a huge range of methods – from supervised methods for learning from labelled data, to statistical pattern analysis and data mining. In this module students will study a range of modern techniques from across the data science spectrum including supervised learning, data mining, and statistical pattern recognition. The module will give the student a good understanding of how, why and when different methods work and experience of applying them in practice.
Learning Outcomes	By the end of the module students should be able to: Understand and explain a range of methods and algorithms for data science Be able to apply a range of algorithms to solve data science problems Compare and contrast different methods, analysing their relative advantages and disadvantages Make informed choices between different methods, given a data science question, and be able to justify these choices.
Assessment	37862-01 : Examination : Exam (Centrally Timetabled) - Written Unseen (80%) 37862-02 : Continuous Assessment : Coursework (20%)
Assessment Methods & Exceptions	Assessment: Main Assessments: Examination (80%) and continuous assessment (20%) Reassessment: Supplementary Assessments: Examination (100%)
Other
Reading List