DEPARTMENT OF ENGINEERING MATHEMATICS

EMATM1400

Pattern Analysis and Statistical Learning

 

(10 Credits)

Timetable Assessment  Syllabus Materials Textbooks Past exams

Organiser:

Dr Tijl de Bie

Lecturer:

Dr Tijl de Bie

Description:

This unit provides first hand experience about the problem of analysing complex real world datasets, like those provided by biology, web, engineering, and many other domains.

 

Students will be exposed to the most recent approaches based on statistical methods, and optimization theory, and to state of the art algorithms. They will also experience real examples of data analysis, based on actual case studies.

Pre-requisites: EMAT10100 or EMAT10702 or equivalent
Aims:

To give students a broad understanding of concepts in pattern analysis and statistics as applied across a range of application domains.

To give students first hand experience in specific algorithms from statistical learning and pattern recognition, including kernel methods, probabilistic graphical models, string analysis, and more.

To teach student the practical application of matlab to pattern analysis problems.

Learning outcomes:

Students will access this unit with a basic knowledge of probability and will acquire working knowledge of practical data analysis, in real world situations.

They will be able to start from a set of data and deliver patterns and other relevant relations detected in it and assessments about their statistical significance.

They will learn general concepts about pattern analysis, that are valid in many domains: algorithmic and statistical principles to be used in different domains.

They will also acquire first hand experience in specific algorithms from statistical learning and pattern recognition, including kernel methods, probabilistic graphical models, string analysis, and more.
They will see how a real world data analysis task is performed, by practicing with real data and matlab.

Organisation & timetable:


Check timetable

Assessments:

50% Formal Examination, 50% Coursework. For each assignment, feedback will be provided at most three weeks after the submission deadline in the form of a letter-code mark, as well as some individual feedback. Furthermore, class-level feedback will be provided in the lecture.
Assessment deadlines and feedback dates appear on Blackboard and SAFE.

Materials: Lecture notes are on Blackboard

Books:

Duda, Hart, Stork, "Pattern Classification", Wiley, 2000

Hastie, Tibshirani, Friedman, "The Elements of Statistical Learning", Springer, 2001.

 

Shawe-Taylor, Cristianini, "Kernel Methods for Pattern Analysis", Cambridge University Press, 2004.

Past exams: See http://www.enm.bris.ac.uk/teaching/local/exam-p-and-s/exams-index.html

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