Programme And Module Handbook
Course Details in 2022/23 Session

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Module Title LM Machine Learning and Intelligent Data Analysis (Extended)
SchoolComputer Science
Department Computer Science
Module Code 06 30255
Module Lead Dr Iain Styles
Level Masters Level
Credits 20
Semester Semester 1
Restrictions None
Contact Hours Lecture-33 hours
Guided independent study-167 hours
Total: 200 hours
Description Machine learning studies how computers can autonomously learn from available data, without being explicitly programmed. The 'information revolution' has generated large amounts of data, but valuable information is often hidden and hence unusable. The module will provide a solid foundation to machine learning and advanced data analysis. It will give an overview of the core concepts, methods, and algorithms for analysing and learning from data. The emphasis will be on the underlying theoretical foundations, illustrated through a set of methods widely used in practice. This will provide the student with a good understanding of how, why and when do various modern machine learning and data analysis methods work.
Learning Outcomes By the end of the module students should be able to:
  • Demonstrate knowledge and understanding of core ideas and foundations of unsupervised and supervised learning on vectorial data
  • explain principles and techniques for mining textual data
  • demonstrate understanding of the principles of efficient web-mining algorithms
  • demonstrate understanding of broader issues of learning and generalisation in machine learning and data analysis systems
  • The student should demonstrate the capacity to independently study, understand, and critically evaluate advanced materials or research articles in the subject areas covered by this module.
Assessment 30255-01 : Examination : Exam (Centrally Timetabled) - Written Unseen (80%)
30255-02 : Continuous Assessment : Coursework (20%)
Assessment Methods & Exceptions Assessment:
Examination (80%),
Continuous Assessment (20%)

Examination (100%)
Reading List