Course Modules
Each PhD student follows a personally tailored programme of postgraduate courses. The course modules cover: financial IT, networks & communications, HCI, computational finance, financial engineering, mathematics, statistics and business, financial mathematics and are supplemented by lectures from our industry partners.
Choices are agreed between the student, their supervisor and the programme director.
The courses undertaken address issues such as technical, analytical and financial knowledge, and international cultural awareness, with the aim of improving students' skills and supporting application to projects within the financial services industry.
Course Modules
Each PhD student follows a personally tailored programme of postgraduate courses. The course modules cover: financial IT, networks & communications, HCI, computational finance, financial engineering, mathematics, statistics and business, financial mathematics and are supplemented by lectures from our industry partners.
Choices are agreed between the student, their supervisor and the programme director.
The courses undertaken address issues such as technical, analytical and financial knowledge, and international cultural awareness, with the aim of improving students' skills and supporting application to projects within the financial services industry.
Courses Available
ELECTIVE MODULES:
Choose 45 credits from these elective modules.
All choices are subject to timetabling constraints and the approval of the relevant Module Tutors (i.e. to ensure any prerequisites are satisfied) and the Programme Director.
UCL COURSES:
Old module code New module code |
Module Title | Term |
COMPG001 COMP0040 |
Financial Data and Statistics |
2 |
COMPG009 COMP0046 |
Networks and Systemic Risk |
2 |
COMPG011 COMP0047 |
Data Analytics |
2 |
COMPGA12 COMP0058 |
Applied Cryptography |
2 |
COMPGC03 COMP0068 |
Architecture and Hardware |
1 |
COMPGC04 COMP0069 |
Systems Infrastructure |
1 |
COMPGC26 COMP0024 |
Artificial Intelligence and Neural Computing |
2 |
COMPGI01 COMP0078 |
Supervised Learning |
1 |
COMPGI08 COMP0080 |
Graphical Models |
1 |
COMPGI09 COMP0081 |
Applied Machine Learning |
2 |
COMPGI13 COMP0083 |
Advanced Topics in Machine Learning |
1 |
COMPGI15 COMP0084 |
Information Retrieval and Data Mining |
2 |
COMPGI16 COMP0085 |
Approximate Inference and Learning in Probabilistic Models |
1 |
COMPGI17 COMP0053 |
Affective Computing and Human-Robot Interaction |
2 |
COMPGI18 COMP0086 |
Probabilistic and Unsupervised Learning |
1 |
COMPGI19 COMP0087 |
Statistical Natural Language Processing |
2 |
COMPGS03 COMP0103 |
Validation & Verification |
2 |
COMPGW01 COMP0123 |
Complex Networks and Web |
1 |
COMPGW02 COMP0124 |
Multi-agent Artificial Intelligence |
2 |
ECONG008 ECON0059 |
Advanced Microeconomic Theory |
2 |
ECONG021 ECON0065 |
Microeconomics |
1 |
MATHGM21 MATH0088 |
Quantitative and Computational Finance |
1 |
MPHYG002 MPHY0022 |
Research Computing with C++ |
2 |
STATG001 STAT0028 |
Statistical Models and Data Analysis |
1 |
STATG004 STAT0031 |
Applied Bayesian Methods |
2 |
STATG011 STAT0010 |
Forecasting |
2 |
STATG012 STAT0008 |
Statistical Inference |
1 |
STATG017 STAT0013 |
Stochastic Methods in Finance |
1 |
STATG019 STAT0017 |
Selected Topics in Statistics |
2 |
OPTIONAL MODULES |
|||
Module |
Title |
Term |
Credits |
COMPGR18 COMP0039 |
Entrepreneurship Theory and Practice |
2 |
15 |
- |
Foreign Language |
15 |
|
EDUCGE01 |
Investigating Research |
2 |
15 |
MPHYG001 MPHY0001 |
Research Software Engineering with Python |
1 |
15 |
EDUCGE02 |
Researcher Professional Development |
1 |
15 |
OPTIONAL MODULES RULES:
Choose 30 credits from these optional modules.
All choices are subject to timetabling constraints and the approval of the relevant Module Tutor (i.e. to ensure any prerequisites are satisfied) and the Programme Director.
The level of the Foreign Language module is to be determined by the UCL Centre for Languages and International Education (CLIE), which determines the module code (www.ucl.ac.uk/clie/CourseUnits).