Unit of study_

STAT3888: Statistical Machine Learning

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 Mathematics and Statistics Academic Operations 6 STAT3914 or STAT3014 STAT2X11 and (DATA2X02 or STAT2X12) None STAT3012 or STAT3912 or STAT3022 or STAT3922 Yes

Teaching staff

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

Assessment

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.

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.

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 Learning outcomes
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)
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.

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.

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

GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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.