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Unit outline_

OCMP5318: Machine Learning and Data Mining

Semester 1a, 2024 [Online] - Online Program

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

Academic unit Computer Science
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
COMP5318 or COMP4318
Assumed knowledge
? 

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

Available to study abroad and exchange students

No

Teaching staff

Coordinator Nataliia Stratiienko, nataliia.stratiienko@sydney.edu.au
The census date for this unit availability is 8 March 2024
Type Description Weight Due Length
Tutorial quiz Mid-Term Quiz
Mid-Term Quiz
15% Week 04 1 hour
Outcomes assessed: LO1
Assignment Assignment 1
Assignment 1
15% Week 04 1000 words
Outcomes assessed: LO1 LO2 LO3
Assignment Assignment 2
Assignment 2
20% Week 06 1000 words
Outcomes assessed: LO1 LO2 LO3 LO4
Supervised exam
? 
Final Exam
Final Exam (Proctored Exam in Canvas)
50% Week 08
Due date: 10 Apr 2024 at 18:30
2 hours
Outcomes assessed: LO1 LO2 LO3 LO4
Tutorial quiz Weekly Knowledge Check Quiz
Weekly Knowledge Check Quiz
0% Weekly 20 mins
Outcomes assessed: LO1 LO4 LO3 LO2

Assessment summary

  • Final Exam: Student will be required to complete essay type questions to test on the basis of their knowledge and understanding at the end of the course.
  • Weekly Knowledge Check Quiz: Student will be required to complete MCQ questions to test on the basis of their knowledge and understanding at the end of the lesson.
  • Mid-Term Quiz: Student will be required to complete MCQ questions to test on the basis of their knowledge and understanding at the middle of the course.
  • Assignment 1: Student will be required to solve pratical problems in a defined timeframe
  • Assignment 2: Student will be required to solve pratical problems in a defined timeframe

Detailed information for each assessment can be found on Canvas.

Assessment criteria


 

Result Name Mark Range Description
High Distinction 85 - 100 Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Distinction 75 - 84 Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Credit 65 - 74

 

Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Pass 50 - 64

 

Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

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.

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.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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 *Weekly Overview *Empirical Risk Minimisation and Regularisation *Linear Regression *Gradient Descent *Logistic Regression *Evaluating ML Models *Weekly Knowledge Check *Reflection *Post Live Session Reflection Independent study (3.3 hr) LO1 LO2 LO3
*Check In *Review Misconceptions/Address Questions *Coding *Review Misconceptions for Programming *Breakout Groups *Synthesis and Closing Workshop (1.5 hr) LO1 LO2 LO3
Week 02 *Weekly Overview *K-Nearest Neighbours *Decision Tree *Ensemble Methods *Weekly Knowledge Check *Reflection *Post Live Session Reflection Independent study (2.7 hr) LO1 LO4
*Objectives and Expectations *Misconceptions *Misconceptions - Programming *Coding Problem Synthesis and Closing Workshop (1.5 hr) LO1 LO4
Week 03 *Weekly Overview *Naive Bayes *Naive Bayes *Support Vector Machine *Non-Linear Support Vector Machine and Kernel Trick *Dimensionality Reduction *Weekly Knowledge Check *Reflection *Post Live Session Reflection Independent study (3 hr) LO1 LO2
*Objectives and Expectations *Misconceptions *Misconceptions - Programming *Coding Problem *Synthesis and Closing Workshop (1.5 hr) LO1 LO2
Week 04 *Weekly Overview *Introduction to Deep Learning *Training Neural Network *Common Techniques and Architectures for Improving Performance of a Neural Network *Weekly Knowledge Check *Reflection *Post Live Session Reflection Independent study (3 hr) LO1 LO2 LO4
*Objectives and Expectations *Misconceptions *Misconceptions - Programming *Coding Problem *Synthesis and Closing Workshop (1.5 hr) LO1 LO2 LO4
Week 05 *Weekly Overview *Introduction to Clustering - Measuring Similarity *Clustering: K-means *Clustering: Hierarchical Algorithms *Clustering: Mixture Models *Weekly Knowledge Check *Reflection *Post Live Session Reflection Independent study (2.25 hr) LO1 LO2
*Objectives and Expectations *Misconceptions *Misconceptions - Programming *Coding Problem *Synthesis and Closing Workshop (1.5 hr) LO1 LO2
Week 06 *Weekly Overview *Reinforcement Learning *Deep Q-Learning *Application of Reinforcement Learning *Weekly Knowledge Check *Reflection *Post Live Session Reflection Independent study (2 hr) LO1 LO2
*Objectives and Expectations *Misconceptions *Misconceptions - Programming *Coding Problem *Synthesis and Closing Workshop (1.5 hr) LO1 LO2

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 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. Students will be able to describe the basic principles, strengths, weaknesses and applicability of machine learning algorithms for solving classification, regression, clustering and reinforcement learning tasks.
  • LO2. Students will be able to design machine learning algorithms through practical experience in designing, implementing and evaluation machine learning algorithms.
  • LO3. Students will be able to design machine learning algorithms for solving classification, regression, clustering, and reinforcement learning tasks using machine learning software and libraries.
  • LO4. Students will be able to 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 changes have been made since this unit was last offered

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.