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BU593C: ARTIFICIAL INTELLIGENCE, MACHINE LEARNING AND FORECASTING (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 and management.

The course will explore recent trends in FINTECH, which are based on data analytics and recent advances in machine learning. The course will provide an introduction to Python, which has become the dominant general-purpose programming language in data analytics and machine learning. To be clear, students are not expected to have any prior knowledge of Python or any other programming language.

Course Details

Study Type Postgraduate Level 5
Session Third Sub Session Credit Points 15 credits (7.5 ECTS credits)
Campus Aberdeen Sustained Study No
Co-ordinators
  • Professor Gerhard Kling

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

  • Any Postgraduate Programme
  • Master Of Business Administration In Business Analytics

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.

Lecture 1: Introduction
Big data, machine learning, artificial intelligence (AI) and its applications in finance and management.

Lecture 2: Getting started with Python
Algorithmic thinking, installation, Anaconda, objects, lists, tuples and arrays, strings

Lecture 3: Structure, control & data
Loops, while / if, array operations, branching, nesting, data import, visualisation of data

Tutorial 1: Analysing data
Data input, importing data, plotting data, descriptive analysis

Lecture 4: What is machine learning?
Types of machine learning, artificial neurons, perception learning algorithm

Lecture 5: Machine learning classifiers
Introduction to scikit-learn, logistic regression, maximum margin, non-linear problems

Tutorial 2: Simple algorithms
Design of a good algorithm, data processing, predictions

Lecture 6: Dimensionality reduction
Principal component analysis (PCA), supervised and unsupervised learning, nonlinear mappings

Lecture 7: Artificial Intelligence
Definitions of AI, building a neural network in Python

Tutorial 3: Time series analysis
Explore financial time series, descriptive analysis in Python, non-stationary time series

Lecture 8: Fintech
Big data in finance, peer-to-peer lending, online / mobile banking, cryptos

Lecture 9: Forecasting
In-sample versus out-of-sample forecasting, comparison of forecasting tools

Tutorial 4: Advanced tools in Python
Advanced codes, projection of new data points, applications

Lecture 10: The future of machine learning and AI
What have we learned about machine learning? What is AI? What are the consequences?


Details for second half-session courses, including assessments, may be subject to change until 23 December 2022.

Contact Teaching Time

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

Teaching Breakdown

  • 3 Seminars during University weeks 42 - 43

More Information about Week Numbers


Details for second half-session courses, including assessments, may be subject to change until 23 December 2022.

Summative Assessments

Essay

Assessment Type Summative Weighting 25
Assessment Weeks Feedback Weeks

Look up Week Numbers

Feedback

Feedback will be provided within 3 weeks of submission.

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.

Computer Programming Exercise

Assessment Type Summative Weighting 75
Assessment Weeks Feedback Weeks

Look up Week Numbers

Feedback

Feedback will be provided within three weeks of submission.

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.

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

Feedback is provided within 3 weeks of submission.

Word Count 3000
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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