Students

COMP783 – Data Science and Machine Learning

2018 – S1 Day

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Convenor, lecturer
Rolf Schwitter
rolf.schwitter@mq.edu.au
Contact via 98509533
E6A333
Wednesday, 16:00 - 17:00
Lecturer
Jia Wu
jia.wu@mq.edu.au
Contact via 98509540
E6A371
Thursday, 16:00 - 17:00
Credit points Credit points
4
Prerequisites Prerequisites
Admission to MRes
Corequisites Corequisites
Co-badged status Co-badged status
ITEC873
Unit description Unit description
This unit introduces the core methods of machine learning and data science for the analysis of both structured and unstructured data. We will learn to use methods for classification, regression, clustering and dimensionality reduction, and will apply them to both numerical and text data. We will also introduce methods for the analysis of "big data" collections, as well as specialised methods for the analysis of sequential data.

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates

Learning Outcomes

On successful completion of this unit, 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.

Assessment Tasks

Name Weighting Hurdle Due
Project proposal presentation 10% No Week 6
Project proposal 10% No Week 7
Project presentation 10% No Week 13
Project report 35% Yes Week 15
Practical exercises 35% No during the semester

Project proposal presentation

Due: Week 6
Weighting: 10%

This is a 10-minute in-class presentation of the project proposal.  It should provide an overview of the project, including the same information as in the project proposal.

You have to submit the slides of your presentation prior to the presentation via iLearn.
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.

Project proposal

Due: Week 7
Weighting: 10%

This proposal should provide the following information about the project:

  • What is the goal of the project?
  • Is there any relevant prior work? (A reference to sections in the textbook might be appropriate)
  • What data set(s) will be used for the project, and where will they be obtained from?
  • What method(s) will be used to analyse the data?  Why are these methods used?

You have to submit the project proposal via iLearn.
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.

Project presentation

Due: Week 13
Weighting: 10%

This in-class presentation should cover all aspects of the project, including results and conclusion.  It should take around 10 minutes.

You have to submit the slides of your presentation prior to the presentation via iLearn.
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.

Project report

Due: Week 15
Weighting: 35%
This is a hurdle assessment task (see assessment policy for more information on hurdle assessment tasks)

This report should describe all aspects of the research project.  It should have the format of a short scientific paper (between 5-8 pages long, plus additional pages of data or graphs if required).  It should contain the following sections:

  • Introduction (including a statement of the problem)
  • Related work (if relevant)
  • Description of the data (including data source)
  • Methods
  • Results
  • Conclusion

This assessment task has a hurdle requirement: you will need to obtain at least 14 marks for the project report in order to pass the unit. If you obtain between 11 and 14 marks for the project report, then you will be given a second (and final) attempt to submit your report. 

Your have to submit the project report via iLearn.
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.

Practical exercises

Due: during the semester
Weighting: 35%

We expect to assign 5 practical exercises during the semester.

You have to submit the solutions to the practical exercises via iLearn.
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.

Delivery and Resources

CLASSES

UNIT WEBPAGE AND TECHNOLOGY USED AND REQUIRED

  • iLearn is going to be used as a main web server for the unit.

Unit Schedule

Week Topic Reading
Week 1 Introduction to the unit  
Week 2 Graphing and plotting Wickham, chapter 2-5
Week 3 Introduction to statistical learning James et al, chapter 2
Week 4 Linear Regression James et al, chapter 3
Week 5 Classification James et al, chapter 4
Week 6 Presentations  
Week 7 Resampling methods James et al, chapter 5
Week 8 Model selection and regularisation James et al, chapter 6
Week 9 Beyond linearity James et al, chapter 7
Week 10 Decision trees, bagging and boosting James et al, chapter 8
Week 11 Support vector machines James et al, chapter 9
Week 12 Unsupervised learning James et al, chapter 10
Week 13 Presentations  

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central (https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policy-central). Students should be aware of the following policies in particular with regard to Learning and Teaching:

Undergraduate students seeking more policy resources can visit the Student Policy Gateway (https://students.mq.edu.au/support/study/student-policy-gateway). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.

If you would like to see all the policies relevant to Learning and Teaching visit Policy Central (https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policy-central).

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/study/getting-started/student-conduct​

Results

Results shown in iLearn, or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit ask.mq.edu.au.

Late Submissions

No extensions will be granted.  Students who have not submitted the task by the deadline will be awarded a zero mark for the task, except for cases in which an application for special consideration is made and approved.

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

Learning Skills

Learning Skills (mq.edu.au/learningskills) provides academic writing resources and study strategies to improve your marks and take control of your study.

Student Services and Support

Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.

Student Enquiries

For all student enquiries, visit Student Connect at ask.mq.edu.au

IT Help

For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/. 

When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.

Graduate Capabilities

PG - Capable of Professional and Personal Judgment and Initiative

Our postgraduates will demonstrate a high standard of discernment and common sense in their professional and personal judgment. They will have the ability to make informed choices and decisions that reflect both the nature of their professional work and their personal perspectives.

This graduate capability is supported by:

Learning outcomes

  • 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.

Assessment tasks

  • Project proposal presentation
  • Project proposal
  • Project presentation
  • Project report
  • Practical exercises

PG - Discipline Knowledge and Skills

Our postgraduates will be able to demonstrate a significantly enhanced depth and breadth of knowledge, scholarly understanding, and specific subject content knowledge in their chosen fields.

This graduate capability is supported by:

Learning outcomes

  • 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.

Assessment tasks

  • Project proposal presentation
  • Project proposal
  • Project presentation
  • Project report
  • Practical exercises

PG - Critical, Analytical and Integrative Thinking

Our postgraduates will be capable of utilising and reflecting on prior knowledge and experience, of applying higher level critical thinking skills, and of integrating and synthesising learning and knowledge from a range of sources and environments. A characteristic of this form of thinking is the generation of new, professionally oriented knowledge through personal or group-based critique of practice and theory.

This graduate capability is supported by:

Learning outcomes

  • 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.

Assessment tasks

  • Project proposal presentation
  • Project proposal
  • Project presentation
  • Project report
  • Practical exercises

PG - Research and Problem Solving Capability

Our postgraduates will be capable of systematic enquiry; able to use research skills to create new knowledge that can be applied to real world issues, or contribute to a field of study or practice to enhance society. They will be capable of creative questioning, problem finding and problem solving.

This graduate capability is supported by:

Learning outcomes

  • 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.

Assessment tasks

  • Project proposal presentation
  • Project proposal
  • Project presentation
  • Project report
  • Practical exercises

PG - Effective Communication

Our postgraduates will be able to communicate effectively and convey their views to different social, cultural, and professional audiences. They will be able to use a variety of technologically supported media to communicate with empathy using a range of written, spoken or visual formats.

This graduate capability is supported by:

Learning outcome

  • Evaluate one or more approaches to advanced topics in machine learning and data science and report the findings in oral and written form.

Assessment tasks

  • Project proposal presentation
  • Project proposal
  • Project presentation
  • Project report

PG - Engaged and Responsible, Active and Ethical Citizens

Our postgraduates will be ethically aware and capable of confident transformative action in relation to their professional responsibilities and the wider community. They will have a sense of connectedness with others and country and have a sense of mutual obligation. They will be able to appreciate the impact of their professional roles for social justice and inclusion related to national and global issues

This graduate capability is supported by:

Learning outcomes

  • 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.

Assessment tasks

  • Project proposal presentation
  • Project proposal
  • Project presentation
  • Project report
  • Practical exercises

Changes from Previous Offering

A hurdle requirement has been specified for the project report.

Assessment standards

COMP783 will be graded according to the following general descriptions of the letter grades as specified by Macquarie University.

• High Distinction (HD, 85-100): Provides consistent evidence of deep and critical understanding in relation to the learning outcomes. There is substantial originality, insight or creativity in identifying, generating and communicating competing arguments, perspectives or problem solving approaches; critical evaluation of problems, their solutions and their implications; creativity in application as appropriate to the program.

• Distinction (D, 75-84): Provides evidence of integration and evaluation of critical ideas, principles and theories, distinctive insight and ability in applying relevant skills and concepts in relation to learning outcomes. There is demonstration of frequent originality or creativity in defining and analysing issues or problems and providing solutions; and the use of means of communication appropriate to the program and the audience.

• Credit (Cr, 65-74): Provides evidence of learning that goes beyond replication of content knowledge or skills relevant to the learning outcomes. There is demonstration of substantial understanding of fundamental concepts in the field of study and the ability to apply these concepts in a variety of contexts; convincing argumentation with appropriate coherent justification; communication of ideas fluently and clearly in terms of the conventions of the program.

• Pass (P, 50-64): Provides sufficient evidence of the achievement of learning outcomes. There is demonstration of understanding and application of fundamental concepts of the program; routine argumentation with acceptable justification; communication of information and ideas adequately in terms of the conventions of the program. The learning attainment is considered satisfactory or adequate or competent or capable in relation to the specified outcomes.

• Fail (F, 0-49): Does not provide evidence of attainment of learning outcomes. There is missing or partial or superficial or faulty understanding and application of the fundamental concepts in the field of study; missing, undeveloped, inappropriate or confusing argumentation; incomplete, confusing or lacking communication of ideas in ways that give little attention to the conventions of the program.

• Fail Hurdle (FH, 49): Student has obtained a raw mark over 50, yet failed all available attempts of at least one hurdle assessment (as described within Schedule 2: Unit Assessment Requirements).

The standards of achievement that will be used to assess each of the assessment tasks with respect to the letter grades are as follows.

Learning outcomes 1, 2 and 3:

P Can formulate and convey most important points that could be expected on the topic.
Cr / D Can formulate and convey clearly all important points that could be expected on the topic.
HD As for Cr or D and can come up with novel insightful points on the topic.

 

Learning Outcomes 4 and 5.

P Be able to write a paper or document, or give a presentation, that would be acceptable at a conference.
Cr / D Be able to write a paper or document, or give a presentation, that would be well received at a conference.
HD Be able to write a paper or document, or give a presentation, that would be well received at a major international conference.

These assessment standards will be used to calculate a numeric mark for each assessed task during marking. 

The total raw mark for the unit will be calculated by summing up the marks for all assessment tasks according to the percentage weightings shown in the assessment summary.

The project report has a hurdle requirement in this unit: you will need to obtain at least 14 marks for the project report in order to pass the unit. If you obtain between 11 and 14 marks for the project report, then you will be given a second (and final) attempt to submit your report.

In order to pass the unit, you need a total raw mark of at least 50%.