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Unit of study_

COMP4318: Machine Learning and Data Mining

Semester 1, 2024 [Normal evening] - Camperdown/Darlington, Sydney

Machine learning is the process of automatically building mathematical models that explain and generalise datasets. It integrates elements of statistics and algorithm development into the same discipline. Data mining is a discipline within knowledge discovery that seeks to facilitate the exploration and analysis of large quantities for data, by automatic and semiautomatic means. This subject provides a practical and technical introduction to machine learning and data mining. Topics to be covered include problems of discovering patterns in the data, classification, regression, feature extraction and data visualisation. Also covered are analysis, comparison and usage of various types of machine learning techniques and statistical techniques.

Unit details and rules

Unit code COMP4318
Academic unit Computer Science
Credit points 6
Prohibitions
? 
COMP5318 OR OCMP5318
Prerequisites
? 
None
Corequisites
? 
Enrolment in a thesis unit. INFO4001 or INFO4911 or INFO4991 or INFO4992 or AMME4111 or BMET4111 or CHNG4811 or CIVL4022 or ELEC4712 or COMP4103 or SOFT4103 or DATA4103 or ISYS4103
Assumed knowledge
? 

Experience with programming and data structures as covered in COMP2123 or COMP2823 or COMP9123 (or equivalent UoS from different institutions)

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Irena Koprinska, irena.koprinska@sydney.edu.au
Lecturer(s) Irena Koprinska, irena.koprinska@sydney.edu.au
Maryam Khanian Najafabadi, maryam.khaniannajafabadi@sydney.edu.au
Type Description Weight Due Length
Supervised exam
? 
hurdle task
Final exam
Exam
60% Formal exam period 2 hours
Outcomes assessed: LO1 LO4
Small continuous assessment Weekly homeworks
Homework exercises
5% Multiple weeks n/a
Outcomes assessed: LO1 LO4
Assignment group assignment Assignment 1
Computer program and report. Group assignment.
10% Week 07
Due date: 08 Apr 2024 at 23:59
n/a
Outcomes assessed: LO1 LO2 LO3
Assignment group assignment Assignment 2
Computer program and report. Group assignment.
25% Week 11
Due date: 10 May 2024 at 23:59
n/a
Outcomes assessed: LO1 LO2 LO3 LO4
hurdle task = hurdle task ?
group assignment = group assignment ?

Assessment summary

  • Weekly homeworks - weekly homeworks submitted online.
  • Assignment 1 – writing a computer program to solve a given task and a report discussing the results.
  • Assignment 2 – writing a computer program to solve a given task and a report discussing the results.
  • Exam – supervised exam during exam period
  • Exam requirement: a minimum of 40% at the exam is required to pass this course

Detailed information for each assessment can be found on Canvas.

Assessment criteria

Result name Mark range Description
High distinction 85-100  
Distinction 75-84  
Credit 65-74  
Pass 50-64  
Fail 0-49 When you don't meet the learning outcomes of the unit to a satisfactory standard.

 

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Weekly homeworks - no late submissions are allowed; Assignment 1 and Assignment 2: Late submissions are allowed up to 3 days late. A penalty of 5% per day late will apply.

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.

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.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

WK Topic Learning activity Learning outcomes
Week 01 Administrative matters and course overview. Introduction to machine learning and data mining. Data: cleaning, pre-processing and similarity measures. Lecture (2 hr) LO1
Week 02 Nearest neighbour. Rule-based algorithms. Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4
Week 03 Logistic regression. Overfitting and regularization. Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4
Week 04 Probability-based learning. Evaluating machine learning methods. Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4
Week 05 Decision trees. Ensembles. Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4
Week 06 Support vector machines. Kernels. Dimensionality reduction methods. Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4
Week 07 Neural networks - perceptrons and backpropagation algorithm. Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4
Week 08 Deep neural networks – convolutional and recurrent. Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4
Week 09 Clustering I: Partitional, model-based and hierarchical. Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4
Week 10 Clustering II: Density-based and grid-based. Evaluating clustering results. Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4
Week 11 Markov models. Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4
Week 12 Reinforcement learning. Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4
Week 13 Revision and exam review. Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4

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.

Required readings

Textbooks:

1. Ian H. Witten, Eibe Frank, Mark Hall and Christopher J. Pal (2017). Data Mining - Practical Machine Learning Tools and Techniques , 4th edition, Morgan Kaufmann.

2. Pang-Ning Tan, Michael Steinbach, Anuj Karpathe and Vipin Kumar (2019). Introduction to Data Mining , 2nd edition, Pearson.


Books for the practical part using Python:

 

1. Andreas C. Mueller and Sarah Guido (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists, O'Reilly.

2. Aurelien Geron (2022). Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow, 2nd edition, O'Reilly.

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. LO1: understand the basic principles, strengths, weaknesses and applicability of machine learning algorithms for solving classification, regression, clustering and reinforcement learning tasks.
  • LO2. LO2: have obtained practical experience in designing, implementing and evaluating machine learning algorithms
  • LO3. LO3: have gained practical experience in using machine learning software and libraries
  • LO4. LO4: present and interpret data and information in verbal and written form

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

No major changes since S1 2023 - positive student feedback. Introducing weekly exercises.

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.