FINANCE 710 : Financial Machine Learning

Business and Economics

2025 Semester Two (1255) (15 POINTS)

Course Prescription

Applies contemporary machine learning techniques to problems in finance. Students will apply and evaluate machine learning models in areas such as predictive modeling and natural language processing. It is recommended that students have prior knowledge of mathematics at the level of MATHS 208 and a basic understanding of finance theory.

Course Overview

This course provides an introduction to machine learning (ML) methods and applications in finance. The course covers fundamental concepts in ML, including supervised and unsupervised learning, as well as advanced topics such as neural networks, deep learning, and natural language processing. Students will learn how to apply these concepts to financial data and build predictive models to support financial decision-making. Upon completing this course, students should be able to analyse financial data programmatically and have experience working with standard ML libraries and packages in Python.

Students with a background in mathematics equivalent to MATHS 208 or similar will find it easier to follow the technical materials presented in this course. While previous coding experience is advantageous, it is not mandatory. A formal academic background in finance is not required; however, 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 3: Knowledge and Practice
Capability 4: Critical Thinking
Capability 5: Solution Seeking
Capability 6: Communication
Graduate Profile: Master of Commerce

Learning Outcomes

By the end of this course, students will be able to:
  1. Understand and apply the concepts and architectures that underpin common supervised and unsupervised machine learning approaches relevant to finance. (Capability 3)
  2. Understand aspects of finance that differentiate it from other machine learning domains. (Capability 3)
  3. Critically analyse financial datasets while considering different ML algorithms' computational requirements and performance limitations. (Capability 4 and 5)
  4. Demonstrate written and oral communication skills to convey machine learning approaches to a wider audience. (Capability 6.1 and 6.2)

Assessments

Assessment Type Percentage Classification
Assignments 30% Group & Individual Coursework
Presentation 20% Group & Individual Coursework
Discussions 10% Individual Coursework
Test 40% Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4
Assignments
Presentation
Discussions
Test

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.

The course consists of 3-hour lectures. All students are expected to come to class prepared and willing to engage in active discussion. Questions are encouraged both during and after lectures.

Delivery Mode

Campus Experience

Attendance is expected at scheduled activities and class participation will count towards final grades.
Lectures will be available as recordings. 
Attendance on campus is not required for the test.
The activities for the course are scheduled as a standard weekly timetable.

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 & Tensorow: Concepts, tools and techniques to build intelligent systems
Second Edition, 2019 (or the Third edition may also be used)
By Aurélien Géron
Published by O’Reilly Media, Inc.

Other learning resources may be posted on Canvas or referenced in the 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.

Incremental improvements will be made to the course based on feedback and industry developments.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework, tests and examinations 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. A student's assessed work may be reviewed against electronic source material using computerised detection mechanisms. Upon reasonable request, students may be required to provide an electronic version of their work for computerised review.

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 http://disability.auckland.ac.nz

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 https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.

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 your assessment is fair, and not compromised. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator, 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 https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html.

Disclaimer

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 you may be asked to submit your 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. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.

Published on 13/11/2024 11:02 a.m.