STATS 709 : Predictive Modelling

Science

2025 Semester Two (1255) (30 POINTS)

Course Prescription

Predictive modelling forecasts likely future outcomes based on historical and current data. Following an advanced introduction to statistics and data analysis, the course will discuss concepts for modern predictive modelling and machine learning.

Course Overview

The course begins with an advanced introduction to probability, statistics and data analysis, including testing, estimation, linear regression, model selection and logistic regression. This lays the foundation for concepts of modern predictive modelling and machine learning such as predictive error, loss functions, overtting, generalisation, regularisation, sparsity. Techniques include modified regression, recursive partitioning, boosting, neural networks. Application to real data sets from a variety of sources, including data preparation and checking, model selection and evaluation, and reporting.

Course Requirements

Prerequisite: COMPSCI 130, MATHS 108, and 15 points from STATS 101, 108, or equivalent Restriction: STATS 201, 207, 208, 210, 225, 707, 765

Capabilities Developed in this Course

Capability 3: Knowledge and Practice
Capability 4: Critical Thinking
Capability 5: Solution Seeking
Capability 6: Communication
Graduate Profile: Master of Data Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Demonstrate and apply scientific thinking, research, theory and practice. (Capability 3)
  2. Think critically and creatively to engage constructively with scientific knowledge systems, practices, theories, and ideas. (Capability 4)
  3. Define problems with regard to their significance, implications and real-world challenges using scientific principles and methods. (Capability 5)
  4. Interpret scientific information, express ideas, and communicate science. (Capability 6)

Assessments

Assessment Type Percentage Classification
Assignments 100% Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4
Assignments

Special Requirements

None.

Workload Expectations

This  is a 30-point course and students are expected to spend 300 hours (equivalent to 150 hours for a standard 15-point course).

Delivery Mode

Online

The course may include live online events including group discussions and/or tutorials and these will be recorded.
Study material will be released progressively throughout the course, typically several weeks ahead of assignment deadlines.
This course runs to the University semester timetable and all the associated completion dates and deadlines will apply.

Learning Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

Textbooks:
• R for Data Science, by Grolemund and Wickham, freely available on their website
• Introduction to Statistical Learning, by James, Witten, Hastie and Tibshirani, freely available on their website

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

Students were happy with the course. There was one assignment that was not clearly explained and that will be remedied for 2025.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework, tests and examinations as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. A student's assessed work may be reviewed against electronic source material using computerised detection mechanisms. Upon reasonable request, students may be required to provide an electronic version of their work for computerised review.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.

Copyright

The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website http://disability.auckland.ac.nz

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and your assessment is fair, and not compromised. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator, and if disruption occurs you should refer to the University Website for information about how to proceed.

The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html.

Disclaimer

Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.

Published on 31/10/2024 08:17 a.m.