Module code: ECS607U

Teaching Staff: Ioannis Patras

Credits: 15.0
Semester: SEM1
Timetable:

Lecture
• Semester 1: Friday 3 pm - 5 pm

Lab
• Semester 1: Weeks 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12: Friday 1 pm - 3 pm

Contact: Dr Ioannis Patras
Overlap: None
Prerequisite: None

Data that has relevance for decision-making is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the Internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and electronic patient records. Data mining is a rapidly growing field that is concerned with developing techniques to assist decision-makers to make intelligent use of these repositories. The field of data mining has evolved from the disciplines of statistics and artificial intelligence.

This course will combine practical exploration of data mining techniques with a exploration of algorithms, including their limitations. Students taking this module should have an elementary understanding of probability concepts and some experience of programming.

Connected course(s): UDF DATA
Assessment: 70.0% Examination, 30.0% Coursework
Level: 6