COMP SCI 7317OL - Using Machine Learning Tools PG

Online - Online Teaching 3 - 2024

In this course, the students will learn about the fundamentals of machine learning and how to utilise and apply some of the most commonly used tools. The students will learn how to create software that allows the use of pre-existing toolkits when appropriate in order to solve a variety of machine learning problems. The course will have a strong practical component, with case studies and worked examples being used to emphasise the importance of legitimate and verifiable solutions.

  • General Course Information
    Course Details
    Course Code COMP SCI 7317OL
    Course Using Machine Learning Tools PG
    Coordinating Unit Computer Science
    Term Online Teaching 3
    Level Postgraduate Coursework
    Location/s Online
    Units 3
    Contact Up to 3 hours per week
    Available for Study Abroad and Exchange
    Prerequisites Carousel 2 Courses: COMP SCI 7211OL, DATA 7301OL, DATA 7302OL & MATHS 7027OL
    Assumed Knowledge Programming experience
    Restrictions Only available to students in the Master Data Sci. (Applied) (OL).
    Assessment Assignments and quizzes
    Course Staff

    Course Coordinator: Nordiana Shah

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes

    On successful completion of this you will be able to:
    1 Adapt industry-standard software tools to model and solve machine learning tasks on real data sets.
    2 Evaluate and identify an appropriate method and tool for a given problem and data set.
    3 Discriminate between problems and data sets that are amenable to machine learning methods and those that are not.
    4 Analyse results and solutions to verify their correctness and identify sources of error.
    5 Assess the significance and validity of solutions obtained by multiple methods.
    6 Design data management procedures to enable the accurate application of machine learning.

    University Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    University Graduate Attribute Course Learning Outcome(s)

    Attribute 1: Deep discipline knowledge and intellectual breadth

    Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.

    2, 3, 4, 5

    Attribute 2: Creative and critical thinking, and problem solving

    Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.

    1, 3, 4, 6

    Attribute 3: Teamwork and communication skills

    Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.


    Attribute 4: Professionalism and leadership readiness

    Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.


    Attribute 5: Intercultural and ethical competency

    Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.

    1, 6

    Attribute 6: Australian Aboriginal and Torres Strait Islander cultural competency

    Graduates have an understanding of, and respect for, Australian Aboriginal and Torres Strait Islander values, culture and knowledge.


    Attribute 7: Digital capabilities

    Graduates are well prepared for living, learning and working in a digital society.

    1, 6

    Attribute 8: Self-awareness and emotional intelligence

    Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.

  • Learning Resources
    Required Resources
    Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, by Aurélien Géron, O'Reilly Media, ISBN: 9781492032649. (Provided by the university library.)
  • Learning & Teaching Activities
    Learning & Teaching Modes
    Teaching and learning modes include weekly online learning content and videos, external resources, practical exercises, group discussions, formative reviews and online tutorials. 

    Students will be able to communicate with the course coordinator, teacher, tutors and other students in the course online discussion form, during synchronised tutorials, in individual video conferences and by email.

    Students are required to be available for synchronous weekly online tutorials.

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    The weekly workload is approx. 25 hours and breaks down into activities as follows:

    Course Content  6.50 hours
    External Resources  1.00 hours
    Practice (Exercises)  5.00 hours
    Discussion  2.00 hours
    Formative Review  1.00 hour
    Tutorial Interaction  1.50 hours
    Video  0.25 hours
    Assessment  10.00 hours

    Learning Activities Summary
    Module 1 Machine Learning Workflow
    Module 2 Looking Inside Machine Learning
    Module 3 Deep Neural Networks
    Module 4 Training Deep Neural Networks
    Module 5 Convolutional Neural Networks
    Module 6 Putting it all together
  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Assessment Task Task Type Due Weighting
    Assignment 1 Machine Learning Case Study
    (Programming and essay questions)
    Summative End of Week 2 30%
    Assignment 2 Deep Neural Networks Training and Evaluation
    (Programming and essay questions)
    Summative End of Week 4 35%
    Assignment 3 Deep Neural Networks Analysis
    (Programming and essay questions)
    Summative End of Week 6 35%
    Assessment Detail
    Assessment 1: Machine Learning Case Study

    In this assessment, you will experiment with a classifier using a standard data set. You will
    analyse the data and evaluate the results obtained by the classifier. Based on your
    understanding of the method and data, you will measure performance and propose steps
    for improvement.

    Assessment 2: Deep Neural Networks: Training and Evaluation

    In this assessment, you will create a deep neural network model using Keras and
    Tensorflow. You will train this model and evaluate its performance against a classical
    baseline method.

    Assessment 3: Deep Neural Networks Analysis

    In this assessment, you will investigate the effects of key parameters on deep neural
    network performance. You will base your investigation on study of the data set and an
    understanding of neural network optimisation. You will reflect on the results of your
    experiments and whether they agree with your predictions.
    Assessments are submitted electronically through the assignment feature in MyUni. Turnitin will be used to automatically check for plagiarism. Concise written feedback and grades will be provided via the MyUni feedback feature.
    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M10 (Coursework Mark Scheme)
    Grade Mark Description
    FNS   Fail No Submission
    F 1-49 Fail
    P 50-64 Pass
    C 65-74 Credit
    D 75-84 Distinction
    HD 85-100 High Distinction
    CN   Continuing
    NFE   No Formal Examination
    RP   Result Pending

    Further details of the grades/results can be obtained from Examinations.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

    Final results for this course will be made available through Access Adelaide.

  • Student Feedback

    The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy ( course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

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