ECMT3185: Semester 2, 2022

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Units ECMT3185 Semester 2 2022 [Normal day]

Unit of study_

ECMT3185: Econometrics of Machine Learning

Overview

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.

Details

Academic unit	Economics
Unit code	ECMT3185
Unit name	Econometrics of Machine Learning
Session, year ?	Semester 2, 2022
Attendance mode	Normal day
Location	Remote
Credit points	6

Enrolment rules

Prohibitions ?	QBUS3820
Prerequisites ?	ECMT2150 and ECMT2160
Corequisites ?	None
Available to study abroad and exchange students	Yes

Teaching staff and contact details

Coordinator	Xuetao Shi, xuetao.shi@sydney.edu.au

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

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	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/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.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

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.

Weekly schedule

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

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

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.

Outcomes
Graduate qualities

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
Learning outcomes

Closing the loop

This unit is being offered for the first time.

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Disclaimer

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.

Current students

Overview

Details

Enrolment rules

Teaching staff and contact details

Assessment

Assessment criteria

Late submission

Special consideration

Academic integrity

Weekly schedule

Study commitment

Required readings

Learning outcomes

Graduate qualities

Outcome map

Closing the loop

Disclaimer

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