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BU551W: ADVANCES IN MACHINE LEARNING IN FINANCE (2021-2022)

Last modified: 31 May 2022 13:05


Course Overview

This course will introduce machine learning, artificial intelligence and forecasting with applications in finance. The course will explore recent trends in FinTech, which are based on data analytics and recent advances in machine learning.

The course is based on Python, which has become the dominant general-purpose programming language in data analytics and machine learning.

Students are required to take CS5076 Introduction to Programming in the first sub-session; hence, the course expects a basic level of programming skills in Python.

Course Details

Study Type Postgraduate Level 5
Session Second Sub Session Credit Points 15 credits (7.5 ECTS credits)
Campus Aberdeen Sustained Study No
Co-ordinators
  • Dr Haofeng Xu

What courses & programmes must have been taken before this course?

  • Master Of Science In Financial Technology
  • ()
  • Any Postgraduate Programme

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

None.

Are there a limited number of places available?

No

Course Description

The course is structured into ten units and associated tutorials.

Unit 1: Introduction

Big data, machine learning, artificial intelligence (AI) and its applications in finance and management.

Unit 2: A Python bootcamp

This unit refreshes some key principles in Python including algorithmic thinking, objects, lists, tuples and arrays, strings, loops, while / if, array operations, branching, nesting, data import, visualisation of data.

Unit 3: APIs and web-scraping

Getting access to online data, APIs to access Yahoo Finance and other sources, access to intra-daily data, implementation in Python

Unit 4: What is machine learning?

Types of machine learning, artificial neurons, perception learning algorithm

Unit 5: Machine learning classifiers

Introduction to scikit-learn, logistic regression, maximum margin, non-linear problems

Unit 6: Dimensionality reduction

Principal component analysis (PCA), supervised and unsupervised learning, nonlinear mappings

Unit 7: Artificial Intelligence

Definitions of AI, building a neural network in Python

Unit 8: Fintech

Big data in finance, peer-to-peer lending, online / mobile banking, cryptos

Unit 9: Forecasting

In-sample versus out-of-sample forecasting, comparison of forecasting tools

Unit 10: Fully automated trading system

Develop a trading bot, testing and implementation of trading strategies


Contact Teaching Time

Information on contact teaching time is available from the course guide.

Teaching Breakdown

More Information about Week Numbers


Details, including assessments, may be subject to change until 31 August 2023 for 1st half-session courses and 22 December 2023 for 2nd half-session courses.

Summative Assessments

Computer Programming Exercise

Assessment Type Summative Weighting 75
Assessment Weeks Feedback Weeks

Look up Week Numbers

Feedback

Written feedback will be provided outlining whether and how students met the learning outcomes.

Learning Outcomes
Knowledge LevelThinking SkillOutcome
ConceptualUnderstandUnderstand the role of big data and its applications in finance and management.
ProceduralEvaluateBy the end of this course students shall critically evaluate the processes and practices of machine learning and artificial intelligence
ReflectionCreateDevelop programming skills in Python to analyse data and create machine learning code.

Essay

Assessment Type Summative Weighting 25
Assessment Weeks Feedback Weeks

Look up Week Numbers

Feedback

Written feedback will be provided outlining whether and how students met the learning outcomes.

Word Count 1500
Learning Outcomes
Knowledge LevelThinking SkillOutcome
ConceptualUnderstandUnderstand the role of big data and its applications in finance and management.
ProceduralEvaluateBy the end of this course students shall critically evaluate the processes and practices of machine learning and artificial intelligence

Formative Assessment

There are no assessments for this course.

Resit Assessments

Essay

Assessment Type Summative Weighting 100
Assessment Weeks Feedback Weeks

Look up Week Numbers

Feedback

Written feedback will be provided outlining wether and how students met the learning outcomes.

Word Count
Learning Outcomes
Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

Course Learning Outcomes

Knowledge LevelThinking SkillOutcome
ProceduralEvaluateBy the end of this course students shall critically evaluate the processes and practices of machine learning and artificial intelligence
ConceptualUnderstandUnderstand the role of big data and its applications in finance and management.
ReflectionCreateDevelop programming skills in Python to analyse data and create machine learning code.

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