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) 鈥 UBNBENSING14
MEng Engineering (Biochemical) 鈥 UMNBENSING14
BEng Engineering (Biomedical) - UBNBMDSING05
MEng Engineering (Biomedical) - UMNBMDSING05
BEng Engineering (Civil) 鈥 UBNCIVSING14
MEng Engineering (Civil) 鈥 UMNCIVSING14
BEng Engineering (Chemical) 鈥 UBNCENSING14
MEng Engineering (Chemical) 鈥 UMNCENSING14
BSc Computer Science 鈥 UBNCOMSING14
MEng Computer Science - UMNCOMSING14
MEng Engineering and Architectural Design 鈥 UMNENGAARD05
BEng Engineering (Electronic and Electrical) 鈥 UBNEENSEEE14
MEng Engineering (Electronic and Electrical) 鈥 UMNEENSEEE14
BEng Engineering (Mechanical) 鈥 UBNMECSING14
MEng Engineering (Mechanical) 鈥 UMNMECSING14
BEng Engineering (Mechanical with Business Finance) 鈥 UBNMECWBFN14
MEng Engineering (Mechanical with Business Finance) 鈥 UMNMECWBFN14
BSc Physics (F300) 鈥 (UBSPHYSING05)
MSci Physics (F303) 鈥 (UMSPHYSING05)
BSc Theoretical Physics (F340) 鈥 (UBSPHYSTPH05)
MSci Theoretical Physics (F345) 鈥 (UMSPHYSTPH05)
BSc Astrophysics (F510) 鈥 (UBSASTSPHY05)
MSci Astrophysics (F511) 鈥 (UMSASTSPHY05)
BSc Chemistry with Mathematics 鈥 (UBSCHEWMAT01)
MSci Chemistry with Mathematics 鈥 (UMSCHEWMAT05)
BSc Mathematics and XXX 鈥 (UBSMATAXXX01)
MSci Mathematics and XXX 鈥 (UMSMATAXXX05)
BSc Mathematics 鈥 (UBSMATSING01)
MSci Mathematics 鈥 (UMSMATSING05)
BSc Mathematics with XXX 鈥 (UBSMATWXXX01)
MSci Mathematics with XXX 鈥 (UMSMATWXXX05)
- Timetable
-
Alternative credit options
There are no alternative credit options available for this module.
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 19th August 2024.
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