FINANCE 781 : Special Topic: Financial Machine Learning

Business and Economics

2022 Semester One (1223) (15 POINTS)

Course Prescription

Students are expected to apply contemporary machine learning methods to topics in finance. The course focuses on the design and implementation of machine learning solutions in the field of finance.

Course Overview

This course seeks to provide students with the skills required to apply contemporary machine learning approaches to problems in finance. This requires an understanding of the concepts underpinning modern machine learning approaches, as well as a familiarity with the tools-of-trade, in particular the Python programming language and machine learning frameworks based thereon. Students will also gain an understanding of those factors that differentiate problems in finance from other machine learning domains such as computer vision, natural language processing and anomaly detection.

Students will be able to follow more of the technical exposition if they are familiar with mathematics at the level of MATHS 208 or similar. Prior coding experience is beneficial but not required. Although there is no formal requirement for prior academic exposure to finance, a practical understanding of finance as it applies to markets, firms and individuals is assumed.

Course Requirements

No pre-requisites or restrictions

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement

Learning Outcomes

By the end of this course, students will be able to:
  1. Understand aspects of finance that differentiates it from other machine learning domains. (Capability 1 and 2)
  2. Learn to use tools commonly used to construct machine learning models, in particular the Python programming language, the Jupiter notebook environment and specialised libraries such as Numpy, Pandas, Scikit-Learn and Tensorflow. (Capability 1 and 3)
  3. Understand and apply the concepts and architectures that underpin common supervised and unsupervised machine learning approaches relevant to finance. (Capability 1 and 3)
  4. Use written communication to convey machine learning approaches to a wider audience. (Capability 2 and 4.2)


Assessment Type Percentage Classification
Online Quizzes 40% Individual Coursework
Assignments 20% Individual Coursework
Final Quiz 40% Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4
Online Quizzes
Final Quiz

Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

Delivery Mode

Campus Experience

Attendance is recommended at scheduled lectures, but will not be enforced. 
The activities for the course are scheduled as a single 3-hour lecture.

Learning Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

The required text for this course is:
Hands-On Machine Learning with Scikit-Learn, Keras & Tensorflow:  Concepts, tools and techniques to build intelligent systems” 
Second Edition, 2019
By Aurélien Géron
Published by O’Reilly Media, Inc.
Note: Since assessment will be open book, having access to the required text is recommended.
In addition, other material may be posted on Canvas or referenced in course material

Student Feedback

At the end of every semester students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback and respond with summaries and actions.

Your feedback helps teachers to improve the course and its delivery for future students.

Class Representatives in each class can take feedback to the department and faculty staff-student consultative committees.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption, we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter


Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course students may be asked to submit coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.

Published on 10/11/2021 11:28 a.m.