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COMP SCI 7317 - Using Machine Learning Tools PG

North Terrace Campus - Trimester 2 - 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 7317
    Course Using Machine Learning Tools PG
    Coordinating Unit Computer Science
    Term Trimester 2
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Contact Up to 3 hours per week
    Available for Study Abroad and Exchange N
    Prerequisites COMP SCI 7202 or (COMP SCI 7210 and COMP SCI 7211); M. Computer Science students are exempted from this pre-req requirement
    Assumed Knowledge Programming experience
    Restrictions Students enrolled in Grad Cert, Grad Dipl and M Cyber Security, M Data Science, Grad Cert, Grad Dip and Master of Artif Intellig& Machine Learning, M Comp & Innovation or M Computer Science, GDip.Eng and M.Eng only
    Assessment Assignments & quizzes
    Course Staff

    Course Coordinator: Dr Dhani Dharmaprani

    Course Timetable

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

  • 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.

    6

    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.

    6

    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 & Teaching Activities
    Learning & Teaching Modes
    This course is delivered in a semester, trimester and intensive format, although enrolment options may be limited by availability.

    This course offers opportunities for you to learn through blended learning approaches, meaning some of the learning is done autonomously online and some of the learning is done through face-to-face engagement. This blended approach is used to create a rich scaffolded and supportive learning experience.
    Workload

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

    This is a 3-unit course. In the semester or trimester format, you are expected to allocate the following study time to fully meet the Course Learning Outcomes (CLOs) for this course. Please note that students work at different paces, so this indicates the approximate time required to complete this course.

    Learning Activity Hours/Week Duration Total
    Online learning activities 1 hour 12 weeks 12 hours
    Face-to-face learning activities 3 hours 12 weeks 36 hours
    Independent study 4 hours 12 weeks 48 hours
    Assessment tasks 5 hours 12 weeks 60 hours
    Expected total student workload 156 hours
    Learning Activities Summary
    You will be required to complete the online learning activities available on MyUni prior to regular face-to-face learning sessions. Throughout these autonomous tasks, you will have time to process new concepts and build foundational knowledge around them. In the face-to-face sessions, you will get a chance to apply that learning to build new skills and address real-world problems.

    Learning activities, both online and face-to-face, are scaffolding to the learning builds throughout the course. Through this learning experience, you will be asked to draw on a range of lower-order and higher-order thinking skills.
  • 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 Weighting Individual/Group Week Due Course Learning Outcomes
    Quiz 5% 3 3, 5
    Predicting Bike Rentals 25% Individual 5 1, 2, 3, 6
    Breast Cancer Classification 30% Individual 8 1, 2, 4, 5
    Quiz 5% 10 3, 5
    Sign Language 35% Individual 12/13 1, 2, 4, 5
    Assessment Detail

    Submission
    Unless otherwise specified, submit all of your assessments to the Assignments space in the MyUni course site for this course. For written assessments, your submissions will go through Turnitin to check for originality. Make sure your submissions adhere to the 成人大片 Academic Integrity policies.
    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 .

  • 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 (http://www.adelaide.edu.au/policies/101/) 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鈥檚 disciplinary procedures.

The 成人大片 is committed to regular reviews of the courses and programs it offers to students. The 成人大片 therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.