Skip to main content
Unit outline_

ECMT3185: Econometrics of Machine Learning

Semester 2, 2022 [Normal day] - Remote

The unit introduces the theory and application of statistical machine learning. Topics covered include supervised versus unsupervised learning; regression and classification; resampling methods including cross-validation and Bootstrap; regularisation and shrinkage approaches such as Lasso; tree-based methods including decision tree and random forest; and support vector machines. The unit focuses on the applications of statistical machine learning in economics, and computer software such as R and Matlab are used throughout the unit.

Unit details and rules

Academic unit Economics
Credit points 6
ECMT2150 and ECMT2160
Assumed knowledge


Available to study abroad and exchange students


Teaching staff

Coordinator Xuetao Shi,
Type Description Weight Due Length
Final exam (Take-home short release) Type D final exam Final exam
Final exam
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4
In-semester test (Take-home short release) Type D in-semester exam Mid-semester exam
Mid-semester exam
20% Week 07
Due date: 15 Sep 2022 at 12:00
1 hour
Outcomes assessed: LO1 LO2 LO3
Assignment Computational assignment
Individual assignment using computational software
30% Week 11
Due date: 23 Oct 2022 at 23:59
One week
Outcomes assessed: LO1 LO2 LO3
Type D final exam = Type D final exam ?
Type D in-semester exam = Type D in-semester exam ?

Assessment summary

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a High distinction indicates work of an exceptional standard, a Distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website  provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.  

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 Introduction Lecture (2 hr) LO1
Week 02 Linear regression Lecture and tutorial (3 hr) LO2 LO3
Week 03 Linear regression Lecture and tutorial (3 hr) LO2 LO3
Week 04 Classification Lecture and tutorial (3 hr) LO2 LO3
Week 05 Resampling methods Lecture and tutorial (3 hr) LO2 LO3
Week 06 Resampling methods Lecture and tutorial (3 hr) LO2 LO3
Week 07 Mid-semester exam Lecture (1 hr) LO1 LO2 LO3
Week 08 Regularization Lecture and tutorial (3 hr) LO2 LO3
Week 09 Regularization Lecture and tutorial (3 hr) LO2 LO3
Week 10 Nonparametric regression Lecture and tutorial (3 hr) LO2 LO3
Week 11 Nonparametric regression Lecture and tutorial (3 hr) LO2 LO3
Week 12 Tree-based methods Lecture and tutorial (3 hr) LO2 LO3
Week 13 Review Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

James, Witten, Hastie, & Tibshirani (2017), An Introduction to Statistical Learning

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. understand the objective of statistical machine learning
  • LO2. understand different machine learning methods, including basic mathematical derivations
  • LO3. identify applications to which certain machine learning methods can be applied
  • LO4. evaluate advantages and disadvantages of different machine learning methods.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities

This section outlines changes made to this unit following staff and student reviews.

This unit is being offered for the first time.


The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.