The content for this Special Topics course is not fixed, but will be determined by the lecturer(s) of the course. Details for a specific offering of the course can be found in the class summary.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

1. Demonstrate a detailed understanding of the topics outlined in the class summary.
2. Demonstrate a high-level mastery of concepts associated with the topics studied, as discussed by the course convener.

Research-Led Teaching

This course provides an introduction to modern machine learning methods with an emphasis on connections to statistical inference. It provides a grounding in the methods underpinning much of modern technology and covers current efforts to connect these to statistical inference, uncertainty quantification and causal inference. Assessment includes practical data modeling projects and readings in current literature. 

Required Resources

Prescribed Text

Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media, 2009. Available on 2-day loan at Chifley Library.

Recommended Resources

Recommended Reading

Zhang, Aston, et al. "Dive into Deep Learning." May 19 (2019): 2019. Available through online open access at https://d2l.ai/

 

Staff Feedback

Student feedback will be given in the following forms in this course:

• Written comments, both individually and to the whole class
• Verbal comments to the whole class

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). The feedback given in these surveys is anonymous and provides the Colleges, University Education Committee and Academic Board with opportunities to recognise excellent teaching, and opportunities for improvement. The Surveys and Evaluation website provides more information on student surveys at ANU and reports on the feedback provided on ANU courses.

Other Information

Consultation time will be finalised at the start of the semester.

Scaling

Your final mark for the course will be based on the raw marks allocated for each of your assessment items. However, your final mark may not be the same number as produced by that formula, as marks may be scaled. Any scaling applied will preserve the rank order of raw marks (i.e. if your raw mark exceeds that of another student, then your scaled mark will exceed the scaled mark of that student), and may be either up or down.

Referencing Requirements

Appropriate referencing will be necessary for the Assignments. For more information see:  http://www.anu.edu.austudents/learning-development/academic-integrity/how-referencing- works

Support for Students

The University offers a number of support services for students. Information on these is available online from http://students.anu.edu.au/studentlife/

Extensions and Penalties

No submission of assessment tasks without an extension after the due date will be permitted. If an assessment task is not submitted by the due date, a mark of 0 will be awarded.

Class Schedule

Week/Session	Summary of Activities	Assessment
1	Machine Learning Overview
2	Regression Models in Statistics
3	Nearest Neighbours and Kernel Methods
4	Classification and Regression Trees
5	Ensembles of trees 1 -- bagging and random forests	Mid-semester project report due (20%)
6	Ensembles of trees 2 -- boosting
7	Diagnostics and interpretation in ML models.	Mid-semester exam (20%)
8	Neural networks
9	Deep learning
10	Statistical inference and predictive models.
11	Ensemble methods and U-statistics
12	Structured regression models and causal inference	Project report due (40%)

Tutorial Registration

Tutorial signup for this course will be done via the Wattle website. Detailed information about signup times will be provided on Wattle or during your first lecture. When tutorials are available for enrolment, follow these steps:

1.      Log on to Wattle, and go to the course site

2.      Click on the link “Tutorial enrolment”

3.      On the right of the screen, click on the tab “Become Member of…..” for the tutorial class you wish to enter

4.      Confirm your choice

If you need to change your enrolment, you will be able to do so by clicking on the tab “Leave group….” and then re-enrol in another group. You will not be able to enrol in groups that have reached their maximum number. Please note that enrolment in ISIS must be finalised for you to have access to Wattle.

Assessment Summary

Assessment task	Value	Due Date	Return of assessment	Learning Outcomes
Class Presentation	40 %	25/02/2020	15/06/2020	1,2
Mid Semester Report	30 %	03/04/2020	17/04/2019	1,2
Final Report	40 %	31/05/2020	15/06/2020	1,2

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details

Policies

ANU has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Misconduct Rule before the commencement of their course. Other key policies and guidelines include:

• Student Assessment (Coursework) Policy and Procedure
• Special Assessment Consideration Policy and General Information 
• Student Surveys and Evaluations
• Deferred Examinations
• Student Complaint Resolution Policy and Procedure

Assessment Requirements

The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the ANU Online website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.

Assessment Task 1

Class Presentation

Students will be asked to give a 20min oral presentation of a paper in the current literature in tutorials. A list of papers will be provided in Week 1 with a schedule of presentations decided in Week 2 when a grading rubric will also be provided; presentations will not begin until Week 4. For review purposes, all presentations will be recorded.

Value: 30% of the Final Raw Mark

Estimated return date: same day

Assessment Task 2

Mid Semester Report

Students will be asked to present an analysis of a real-world data set as an individual project.Potential data and a rubric will be provided in Week 2. Midterm report should be submitted in Wattle and requires selection of a data set and preliminary analysis and plan for future work.

Assessment Task 3

Final Report

Primary task in the class is a practical data analysis applying ML methods to understand a large data set. Final report must demonstrate both a sound use of ML methods, intelligent questions and careful assessment of uncertainty. This report should be conducted individually and submitted through Wattle.

Academic Integrity

Academic integrity is a core part of the ANU culture as a community of scholars. At its heart, academic integrity is about behaving ethically, committing to honest and responsible scholarly practice and upholding these values with respect and fairness.

The ANU commits to assisting all members of our community to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. The ANU expects staff and students to be familiar with the academic integrity principle and Academic Misconduct Rule, uphold high standards of academic integrity and act ethically and honestly, to ensure the quality and value of the qualification that you will graduate with.

The Academic Misconduct Rule is in place to promote academic integrity and manage academic misconduct. Very minor breaches of the academic integrity principle may result in a reduction of marks of up to 10% of the total marks available for the assessment. The ANU offers a number of online and in person services to assist students with their assignments, examinations, and other learning activities. Visit the Academic Skills website for more information about academic integrity, your responsibilities and for assistance with your assignments, writing skills and study.

Online Submission

You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education) submission must be through Turnitin.

Hardcopy Submission

For some forms of assessment (hand written assignments, art works, laboratory notes, etc.) hard copy submission is appropriate when approved by the Associate Dean (Education). Hard copy submissions must utilise the Assignment Cover Sheet. Please keep a copy of tasks completed for your records.

Late Submission

No submission of assessment tasks without an extension after the due date will be permitted. If an assessment task is not submitted by the due date, a mark of 0 will be awarded.

Referencing Requirements

Accepted academic practice for referencing sources that you use in presentations can be found via the links on the Wattle site, under the file named “ANU and College Policies, Program Information, Student Support Services and Assessment”. Alternatively, you can seek help through the Students Learning Development website.

Returning Assignments

Assignments will be returned online.

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Resubmission of Assignments

Assignments may not be resubmitted.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.

Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

• ANU Health, safety & wellbeing for medical services, counselling, mental health and spiritual support
• ANU Diversity and inclusion for students with a disability or ongoing or chronic illness
• ANU Dean of Students for confidential, impartial advice and help to resolve problems between students and the academic or administrative areas of the University
• ANU Academic Skills and Learning Centre supports you make your own decisions about how you learn and manage your workload.
• ANU Counselling Centre promotes, supports and enhances mental health and wellbeing within the University student community.
• ANUSA supports and represents undergraduate and ANU College students
• PARSA supports and represents postgraduate and research students