果冻影院

XClose

果冻影院 Module Catalogue

Menu

Data Mining and Analysis (BENG0095)

Key information

Faculty
Faculty of Engineering Sciences
Teaching department
Biochemical Engineering
Credit value
15
Restrictions
1. Code(s) of any module which is a pre-requisite? ENGS203P or BENG206P or COMP206P or other suitable Year 2 or Year 3 course covering differential and integral calculus 2. Required A-level subjects? Mathematics 3. Any other restriction (i.e. module available to all Engineering students in FES)? BEng Engineering (Biochemical) - UBNBENSING14 MEng Engineering (Biochemical) - UMNBENSING14 BEng Engineering (Biomedical) - UBNBMDSING05 MEng Engineering (Biomedical) - UMNBMDSING05 BEng Engineering (Civil) - UBNCIVSING14 MEng Engineering (Civil) - UMNCIVSING14 BEng Engineering (Chemical) - UBNCENSING14 MEng Engineering (Chemical) - UMNCENSING14 BSc Computer Science - UBNCOMSING14 MEng Computer Science - UMNCOMSING14 MEng Engineering and Architectural Design - UMNENGAARD05 BEng Engineering (Electronic and Electrical) - UBNEENSEEE14 MEng Engineering (Electronic and Electrical) - UMNEENSEEE14 BEng Engineering (Mechanical) - UBNMECSING14 MEng Engineering (Mechanical) - UMNMECSING14 BEng Engineering (Mechanical with Business Finance) - UBNMECWBFN14 MEng Engineering (Mechanical with Business Finance) - UMNMECWBFN14 BSc Physics (F300) - (UBSPHYSING05) MSci Physics (F303) - (UMSPHYSING05) BSc Theoretical Physics (F340) - (UBSPHYSTPH05) MSci Theoretical Physics (F345) - (UMSPHYSTPH05) BSc Astrophysics (F510) - (UBSASTSPHY05) MSci Astrophysics (F511) - (UMSASTSPHY05) BSc Chemistry with Mathematics - (UBSCHEWMAT01) MSci Chemistry with Mathematics - (UMSCHEWMAT05) BSc Mathematics and XXX - (UBSMATAXXX01) MSci Mathematics and XXX - (UMSMATAXXX05) BSc Mathematics - (UBSMATSING01) MSci Mathematics - (UMSMATSING05) BSc Mathematics with XXX - (UBSMATWXXX01) MSci Mathematics with XXX - (UMSMATWXXX05)
Timetable

Alternative credit options

There are no alternative credit options available for this module.

Description

This module will provide a 鈥渞oadmap鈥� of machine learning techniques for a wide variety of applications. Up until the turn of the 21st century large datasets appeared in a limited number of real-life applications. Currently, the amount of data collected daily around the world (whether experimental, behavioral, internet based or through databases) has grown exponentially. The challenge now lies in the development of efficient computational methods able to organize the gamut of available information in a way that will allow us to discern meaningful correlations and useful knowledge.

On successfully completing the module, students will be able to:

  • Determine the machine learning techniques which most appropriately address a real world problem
  • Display an understanding of different machine learning tasks and the algorithms most appropriate for addressing them.
  • Critique the results of a machine learning exercise.
  • Apply the techniques of regression, classification, clustering, and dimensionality reduction on real world data
  • Apply machine learning software and toolkits in a range of applications
  • Carry out problem solving on a piece of practical work that requires the application of machine learning techniques.

Module deliveries for 2024/25 academic year

Intended teaching term: Term 1 听听听 Undergraduate (FHEQ Level 6)

Teaching and assessment

Mode of study
In person
Methods of assessment
0% In-class activity
50% Coursework
50% Exam
Mark scheme
Numeric Marks

Other information

Number of students on module in previous year
68
Module leader
Dr Yuhong Zhou
Who to contact for more information
beugadmin@ucl.ac.uk

Last updated

This module description was last updated on 8th April 2024.

听

Useful links

Sustainability

For a list of modules related to climate change as well as social and environmental sustainability at 果冻影院, type 'climate' or 'sustainability' into the search. Visit Sustainable 果冻影院 for information on extra-curricular activity on sustainability.

Important information

The catalogue has been updated with key information about the modules that will run during the 2024/25 academic session. Current and prospective students can browse through the catalogue to consider possible module choices for the coming year.

Please note,听information in the catalogue is听subject to change as teaching and assessment arrangements for 2024/25 may need to be adjusted in line with the University's Feedback and Assessment principles and operating model.

Centrally managed exam durations will be provided on students' individual exam timetables. Arrangements for locally managed exams and in-class activity will be confirmed by the teaching department for the module.

听

听