STAT3888: Semester 2, 2020

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Units STAT3888 Semester 2 2020 [Normal day]

Unit of study_

STAT3888: Statistical Machine Learning

Semester 2, 2020 [Normal day] - Camperdown/Darlington, Sydney

Overview

Data Science is an emerging and inherently interdisciplinary field. A key set of skills in this area fall under the umbrella of Statistical Machine Learning methods. This unit presents the opportunity to bring together the concepts and skills you have learnt from a Statistics or Data Science major, and apply them to a joint project with NUTM3888 where Statistics and Data Science students will form teams with Nutrition students to solve a real world problem using Statistical Machine Learning methods. The unit will cover a wide breadth of cutting edge supervised and unsupervised learning methods will be covered including principal component analysis, multivariate tests, discrimination analysis, Gaussian graphical models, log-linear models, classification trees, k-nearest neighbors, k-means clustering, hierarchical clustering, and logistic regression. In this unit, you will continue to understand and explore disciplinary knowledge, while also meeting and collaborating through project-based learning; identifying and solving problems, analysing data and communicating your findings to a diverse audience. All such skills are highly valued by employers. This unit will foster the ability to work in an interdisciplinary team, and this is essential for both professional and research pathways in the future.

Unit details and rules

Unit code	STAT3888
Academic unit	Mathematics and Statistics Academic Operations
Credit points	6
Prohibitions ?	STAT3914 or STAT3014
Prerequisites ?	STAT2X11 and (DATA2X02 or STAT2X12)
Corequisites ?	None
Assumed knowledge ?	STAT3012 or STAT3912 or STAT3022 or STAT3922
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	John Ormerod, john.ormerod@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Open book)	Examination Online open book without invigilation	40%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Assignment 1 Exploratory data analysis of data set used in Major project.	5%	Week 05 Due date: 25 Sep 2020 at 17:00 Closing date: 02 Oct 2020	4 weeks
Outcomes assessed:
Assignment	Assignment 2 Mini report analyzing small clean data set using ML methods.	5%	Week 09 Due date: 30 Oct 2020 at 17:00 Closing date: 06 Nov 2020	4 Weeks
Outcomes assessed:
Assignment	Major project - Peer to peer/reflection Used to assess individual contributions	10%	Week 11 Due date: 09 Nov 2020 at 17:00 Closing date: 16 Nov 2020	Short survey/500 words
Outcomes assessed:
Assignment	Major project - Manuscript Statistical analysis of nutrition data set	15%	Week 12 Due date: 20 Nov 2020 at 17:00 Closing date: 27 Nov 2020	4000 words
Outcomes assessed:
Assignment	Major project - Multimedia communication Communication of project results to a public audience	15%	Week 12 Due date: 20 Nov 2020 at 17:00 Closing date: 27 Nov 2020	500 words or 3 minutes
Outcomes assessed:
Presentation	Major project - presentation Group presentation of results (slides submitted at given due date)	10%	Week 12 Due date: 16 Nov 2020 at 17:00 Closing date: 23 Nov 2020	5 minutes + 2 mins for questions
Outcomes assessed:
= group assignment ? = Type C final exam ?

Assessment summary

Examination: This exam will test the learning outcomes attained in lectures, and tutorials/computer labs. University-approved non-programmable calculators may be used.
Computer lab reports: There are 2 computer lab reports, which must be submitted electronically in Turnitin, via the Learning Management System (Canvas) website, by the deadline. Note that a submission will not be marked if it is illegible, sideways or upside down. It is your responsibility to check your submission receipt (which will be automatically emailed to you) to ensure that your report has been submitted correctly.
Major project: The major project is broken up into several assessment items: major report, multimedia item, presentation, meeting minutes, peer-to-peer review, group work attendance, and short reflection.

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.

For more information see sydney.edu.au/students/guide-to-grades.

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.

Learning support

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction, administration and motivation	Lecture (1 hr)
	Data cleaning	Lecture (1 hr)
	Unsupervised learning - Introduction to clustering	Lecture (1 hr)
Week 02	Unsupervised learning - K-means	Lecture (1 hr)
	Unsupervised learning - Model based clustering	Lecture (1 hr)
	Unsupervised learning - Hierarchical clustering	Lecture (1 hr)
	Tutorial/Lab - Clustering	Tutorial (1 hr)
Week 03	Unsupervised learning - PCA background	Lecture (1 hr)
	Unsupervised learning - Principal component analysis	Lecture (1 hr)
	Unsupervised learning - Dimension reduction	Lecture (1 hr)
	Group meeting - Group formation	Workshop (2 hr)
	Tutorial/Lab - Dimension reduction	Tutorial (1 hr)
Week 04	Supervised learning - Introduction to supervised learning	Lecture (1 hr)
	Supervised learning - Logistic regression	Lecture (1 hr)
	Supervised learning - Penalized Logistic regression	Lecture (1 hr)
	Group work - Project brain strorming	Workshop (2 hr)
	Tutorial/Lab - Logistic regression	Workshop (1 hr)
Week 05	Supervised learning - Discrimination analysis	Lecture (1 hr)
	Supervised learning - Regression and classification trees	Lecture (1 hr)
	Supervised learning - Random forests	Lecture (1 hr)
	Group work - Preliminary analysis	Workshop (2 hr)
	Tutorial/Lab - Discrimination analysis and classification trees	Tutorial (1 hr)
Week 06	Network models - Introduction to graphical models	Lecture (1 hr)
	Network models - Introduction to graphical models	Lecture (1 hr)
	Group work - Preliminary analysis	Workshop (2 hr)
	Tutorial/Lab - Graphical models	Tutorial (1 hr)
Week 07	Network models - Log-linear models	Lecture (1 hr)
	Network models - Log-linear models	Lecture (1 hr)
	Group work - Project proposal	Workshop (2 hr)
	Tutorial/Lab - Log-linear models	Tutorial (1 hr)
Week 08	Network models - Simpson's paradox	Lecture (1 hr)
	Network models - Introduction to causal inference	Lecture (1 hr)
	Group work	Workshop (2 hr)
	Tutorial/Lab - Causal inference	Tutorial (1 hr)
Week 09	Probabilistic learning - Introduction to Bayesian inference	Lecture (1 hr)
	Probabilistic learning - Introduction to Bayesian inference	Lecture (1 hr)
	Group work	Workshop (2 hr)
	Tutorial/Lab - Bayesian inference	Tutorial (1 hr)
Week 10	Probabilistic learning - Introduction to MCMC	Lecture (1 hr)
	Probabilistic learning - Introduction to MCMC	Lecture (1 hr)
	Group work	Workshop (2 hr)
	Tutorial/Lab - MCMC	Tutorial (1 hr)
Week 11	Probabilistic learning - Bayesian modelling	Lecture (1 hr)
	Probabilistic learning - Bayesian modelling	Lecture (1 hr)
	Group work - Finalize major project	Workshop (2 hr)
	Tutorial/Lab - Bayesian modelling	Tutorial (1 hr)
Week 12	Presentations	Workshop (2 hr)
Week 12	Presentations	Workshop (2 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.

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. apply disciplinary knowledge in statistics and data science to solve problems in an interdisciplinary context (nutrition)
LO2. find, define, and delimit authentic problems in order to address them
LO3. create an investigation strategy, explore solutions, discuss approaches, and predict outcomes
LO4. apply, formulate, interpret, and compare statistical machine learning methods including (wherever relevant) evaluation of model appropriateness
LO5. demonstrate integrity, confidence, personal resilience, and the capacity to manage challenges, both individually and in teams
LO6. collaborate with diverse groups across cultural and disciplinary boundaries to develop solution(s) to the project problems
LO7. communicate project outcomes effectively to a broad audience
LO8. identify appropriate machine learning problems to a particular problem, and judge the appropriateness of model evaluation procedures.

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

Responding to student feedback

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

No changes have been made since this unit was last offered.

Additional information

Where to go for help: For help with statistics, you can post a question on the Ed forum (anonymously from other students if you prefer), ask your tutor during a tutorial, computer lab (or workshop, for STAT3888), consult the lecturer in their consultation time (see above), or email john.ormerod@sydney. edu.au. For administrative questions, first check carefully whether the answers are on this information sheet or on the STAT3888 webpage; if not, ask on the ED forum or (if the question is specific to your situation) ask at the Student Services Office (Carslaw 520) or email STAT3888@sydney.edu.au or STAT3914@sydney.edu.au as appropriate. Ensure that any emails that you send contain your name and SID, because anonymous emails will be ignored. If your email includes questions that other students would benefit from seeing the answers to, you may be asked to post them on the Ed forum so that they can be answered there.

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

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

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