OCMP5318: Semester 1a, 2023

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Units OCMP5318 Semester 1a 2023 [Online]

Unit of study_

OCMP5318: Machine Learning and Data Mining

Semester 1a, 2023 [Online] - Online Program

Overview

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	OCMP5318
Academic unit	Computer Science
Credit points	6
Prohibitions ?	COMP5318 or COMP4318
Prerequisites ?	None
Corequisites ?	None
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

Type	Description	Weight	Due	Length
Tutorial quiz	Mid-Term Quiz Mid-Term Quiz	15%	Week 04	1 hour
Outcomes assessed:
Assignment	Assignment 1 Assignment 1	15%	Week 05	1000 words
Outcomes assessed:
Monitored exam ?	Final Exam Final Exam	50%	Week 08 Due date: 13 Apr 2023 at 18:30	2 hours
Outcomes assessed:
Assignment	Assignment 2 Assignment 2	20%	Week 08	1000 words
Outcomes assessed:
Tutorial quiz	Weekly Knowledge Check Quiz Weekly Knowledge Check Quiz	0%	Weekly	20 mins
Outcomes assessed:

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.

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.

Learning support

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.

Weekly schedule

WK	Topic	Learning activity
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)
Week 01	Check In Review Misconceptions/Address Questions Coding Review Misconceptions for Programming Breakout Groups Synthesis and Closing	Workshop (1.5 hr)
Week 02	Weekly Overview K-Nearest Neighbours Decision Tree Ensemble Methods Weekly Knowledge Check Reflection *Post Live Session Reflection	Independent study (2.7 hr)
Week 02	Objectives and Expectations Misconceptions Misconceptions - Programming Coding Problem Synthesis and Closing	Workshop (1.5 hr)
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)
Week 03	Objectives and Expectations Misconceptions Misconceptions - Programming Coding Problem *Synthesis and Closing	Workshop (1.5 hr)
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)
Week 04	Objectives and Expectations Misconceptions Misconceptions - Programming Coding Problem *Synthesis and Closing	Workshop (1.5 hr)
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)
Week 05	Objectives and Expectations Misconceptions Misconceptions - Programming Coding Problem *Synthesis and Closing	Workshop (1.5 hr)
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)
Week 06	Objectives and Expectations Misconceptions Misconceptions - Programming Coding Problem *Synthesis and Closing	Workshop (1.5 hr)

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

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.

Outcomes
Graduate qualities

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
Learning outcomes

Responding to student feedback

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

This is the first time this unit has been 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.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

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