COMP SCI 7206A - Artificial Intelligence and Machine Learning Industry Project Part A

North Terrace Campus - Trimester 1 - 2024

This is a project course giving students the chance to conduct applied research in a real world artificial intelligence and machine learning problem domain. As part of the project students will present their work to an audience and write a major report detailing their results. The project is conducted individually under the guidance of an academic supervisor and may also involve an industrial partner. Project topics vary from year to year depending on staff and industry supervisor availability. It will be better if students have some working experience or are currently employed by companies.

  • General Course Information
    Course Details
    Course Code COMP SCI 7206A
    Course Artificial Intelligence and Machine Learning Industry Project Part A
    Coordinating Unit Computer Science
    Term Trimester 1
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Contact Meetings as negotiated with supervisors.
    Available for Study Abroad and Exchange N
    Restrictions Available to Master Machine Learning students only.
    Assessment Research Project, reports, outcomes and presentations.
    Course Staff

    Course Coordinator: Dr Ehsan Abbasnejad

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    Upon completing this course, you will be able to:
    1. Identify and evaluate a current, specialised problem in Artificial Intelligence and Machine Learning Industry and devise an appropriate software development or software management solution
    2. Contextualise a software development or management project within the relevant academic literature and industry context
    3. Implement an industry software development or management project with clear milestones
    4. Appraise and select appropriate software development methodologies for a real-world software development or management project
    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.

    1, 2, 3, 4

    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, 2, 3, 4

    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.

    3, 4

    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.


    Attribute 7: Digital capabilities

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

    1, 2, 3, 4
  • Learning & Teaching Activities
    Learning & Teaching Modes
    Working with industry supervisor to achieve project goals.
    Working with academic supervisor who will provide on-going guidance and feedback.
    The project work may involve face-to-face or online meetings with both the industry and academic supervisors.
    Communicating with your peers and the course coordinator, who will post announcements and answer general course questions.


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

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

    This is a 3-unit course and typical workload is 156 hours over the whole trimester.  Please note that students work at different paces, so this indicates the approximate time required to complete this course.
    Learning Activities Summary
    Learning activities include:
    • Meeting with industry and academic supervisors.
    • Reading scientific and engineering literature
    • Preparing and delivering presentations.
    • Writing reports.
  • 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
    You will complete three assessments: a project proposal, a mid-term project review, and a final report. These assessments have been structured to support you to develop your research project. The feedback you receive on each submission will help you further refine your ideas and progress your project.
    Assessment Detail
    Assessment Weighting Individual/Group Due
    Assessment 1: Project proposal 25% Individual Week 3
    Assessment 2: Mid-term progress report 25% Individual Week 7
    Assessment 3: Term Final report 50% Individual Week 12
    Submission using myUni assessment page.
    Course Grading

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

    NOG (No Grade Associated)
    Grade Description
    CN Continuing

    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

    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.

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