Late Assessment Submission Penalty 

From 1 July 2022, Students enrolled in Session based units with written assessments will have the following late penalty applied. Please see https://students.mq.edu.au/study/assessment-exams/assessments for more information. 

Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark) will be applied each day a written assessment is not submitted, up until the 7^th day (including weekends). After the 7^th day, a grade of '0' will be awarded even if the assessment is submitted. Submission time for all written assessments is set at 11:55 pm. A 1-hour grace period is provided to students who experience a technical concern. 

For any late submission of time-sensitive tasks, such as scheduled tests/exams, performance assessments/presentations, and/or scheduled practical assessments/labs, students need to submit an application for Special Consideration.  

Assessments where Late Submissions will be accepted 

In this unit, late submissions will be accepted as follows: 

• Practical Exercises where the submission is a written report, notebook, etc – YES, Standard Late Penalty applies 
• Practical Exercises where the submission is in the form of an online quiz - NO, unless Special Consideration is granted 

• Major Project – YES, Standard Late Penalty applies 
• Exam - NO, unless Special Consideration is granted 

 

Assessment Tasks

Name	Weighting	Hurdle	Due
Practical Exercises	30%	No	throughout the semester
Major Project	40%	No	week 12
Exam	30%	No	exam period

Practical Exercises

Assessment Type ¹: Problem set Indicative Time on Task ²: 30 hours Due: throughout the semester Weighting: 30%

 

These will consist of practical exercises set throughout the semester.

 

On successful completion you will be able to:

• Derive algorithms to solve machine learning problems based on an understanding of how machine learning and data science problems are mathematically formulated and analysed.
• Create machine learning solutions to data science problems by identifying and applying appropriate algorithms and implementations.

Major Project

Assessment Type ¹: Project Indicative Time on Task ²: 30 hours Due: week 13 Weighting: 40%

 

The student will apply knowledge of conventional machine learning and deep learning to design and implement a solution to a (classification or other) task on a defined dataset. The deliverables will be the implementation and a report describing this implementation.

 

On successful completion you will be able to:

• Derive algorithms to solve machine learning problems based on an understanding of how machine learning and data science problems are mathematically formulated and analysed.
• Create machine learning solutions to data science problems by identifying and applying appropriate algorithms and implementations.
• Analyse real-world data science problems, identify which methods are appropriate, organise the data appropriately, apply one or more methods, and evaluate the quality of the solution.
• Evaluate one or more approaches to advanced topics in machine learning and data science and report the findings in oral and written form.

Exam

Assessment Type ¹: Examination Indicative Time on Task ²: 2 hours Due: exam period Weighting: 30%

 

The examination will require students to understand, apply, analyse and evaluate material drawn from the unit topics.

 

On successful completion you will be able to:

• Derive algorithms to solve machine learning problems based on an understanding of how machine learning and data science problems are mathematically formulated and analysed.
• Create machine learning solutions to data science problems by identifying and applying appropriate algorithms and implementations.
• Analyse real-world data science problems, identify which methods are appropriate, organise the data appropriately, apply one or more methods, and evaluate the quality of the solution.
• Evaluate one or more approaches to advanced topics in machine learning and data science and report the findings in oral and written form.

¹ If you need help with your assignment, please contact:

• the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
• the Writing Centre for academic skills support.

² Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation

Classes

• Classes: There will be a two hour lecture each week, and additionally a small class that will focus on working through practical tasks.
• Textbook: The main textbook for the unit is Aurélien Géron (2019)  "Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow" (2nd edition; September 2019).  This is available through the MQ library (MQ has an arrangement with publisher O'Reilly: you can register at O'Reilly using your MQ email, and get access to the book there).  The book comes with source code that is available from https://github.com/ageron/handson-ml2. A supplementary source of material for a deeper understanding of the theoretical material is Trevor Hastie, Robert Tibshirani and Jerome Friedman (2009; corrected 12th printing Jan 2017) "The Elements of Statistical Learning: Data Mining, Inference, and Prediction."  A freely downloadable pdf is available at the first author's webpage.

Background Material

• The unit requires a sound background in programming, and particularly Python.  If you feel you need a refresher on Python (or an introduction from scratch, as long as you're a quick and independent learner), there's a popular tutorial at http://learnpython.org/.  This goes all the way from basic programming to the mathematical and data science libraries used by Python, like numpy and pandas.  There's also the resources at the Python website at python.org, like the Beginner's Guide.
• For a refresher on linear algebra as it is relevant to machine learning, Jason Brownlee (2018) "Basics of Linear Algebra for Machine Learning" has useful material that's linked to Python data structures.  (The book used to have a freely available pdf, but this seems to have disappeared.  It is published by Machine Learning Mastery.)

Unit Webpage and Technology Used and Required

• iLearn is going to be used as a main web server for the unit.
• The programming language for the unit will be Python.  The "conventional" machine learning section will use Python's scikit-learn, and the deep learning section will use TensorFlow and Keras.
• For the most part, programming will be done via Jupyter notebooks.  We'll typically be running these notebooks on Google Colab.

Methods of Communication

We will communicate with you via your university email and through announcements on iLearn. Queries to convenors can either be placed on the iLearn discussion board or sent to the unit convenor via the contact email on iLearn.

COVID Information

For the latest information on the University’s response to COVID-19, please refer to the Coronavirus infection page on the Macquarie website: https://www.mq.edu.au/about/coronavirus-faqs. Remember to check this page regularly in case the information and requirements change during semester. If there are any changes to this unit in relation to COVID, these will be communicated via iLearn..

Week	Topic	Readings (from Géron)
1	What is Machine Learning?	Ch 1
2	Workflow of a Machine Learning Project	Ch 2
3	Support Vector Machines and Decision Trees	Ch 3 - 6
4	Ensemble Learning, Random Forests, and Dimensionality Reduction	Ch 7 - 8
5	Handling Text Data	supplementary notes
6 - 7	Introduction to Artificial Neural Networks: ANN basics Multi-Layer Perceptrons The Tensorflow and Keras frameworks	Ch 10 - 11
8 - 9	Deep Neural Networks The structure of deep NNs Convolutional NNs Practical issues in training NNs	Ch 11 - 14, supplementary notes
10	NNs for sequences, and advanced topics: Recurrent NNs Autoencoders	Ch 15 and onwards, supplementary notes
11	Transformers	Supplementary notes
12	Machine Learning and security/privacy	Supplementary notes
13	Unit review