TECH 1004 - Artificial Intelligence Technologies

North Terrace Campus - Semester 2 - 2021

This course will provide students with introductory knowledge and skills in the application of modern AI tools and techniques. The course introduces Python, a key language for developing modern AI applications. The course then demonstrates how to run, modify and build Python implementations of current AI technologies including, standard and new machine learning and deep learning tools. The course will have a strong emphasis how to best make use of the large range of materials, and tutorials that are released with new AI frameworks. In particular, the course will develop a high-level understanding of the key concepts and terminology allowing students to make use of new frameworks as they emerge. Assessment can include practical exercises, workshops, case studies and a final exam.

  • General Course Information
    Course Details
    Course Code TECH 1004
    Course Artificial Intelligence Technologies
    Coordinating Unit TBS
    Term Semester 2
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 4 hours per week
    Available for Study Abroad and Exchange N
    Incompatible COMP SCI 3007
    Restrictions Available only to students in Associate Degree/Diploma/Bachelor of Technology (Defence Industries)
    Course Description This course will provide students with introductory knowledge and skills in the application of modern AI tools and techniques. The course introduces Python, a key language for developing modern AI applications. The course then demonstrates how to run, modify and build Python implementations of current AI technologies including, standard and new machine learning and deep learning tools. The course will have a strong emphasis how to best make use of the large range of materials, and tutorials that are released with new AI frameworks. In particular, the course will develop a high-level understanding of the key concepts and terminology allowing students to make use of new frameworks as they emerge. Assessment can include practical exercises, workshops, case studies and a final exam.
    Course Staff

    Course Coordinator: Dr Wei Zhang

    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 course students will be able to:
    1.Select, run, modify and build standard Python programs to solve relevant problems using AI or machine learning
    2.Identify and use a broad range of existing resources in the development of Ai and machine learning programs
    3.Explain key concepts, differences, limitations and opportunities of various AI and machine learning approaches
    4. Applying norms to the use of AI and machine learning including considerations of ethics, privacy and security
    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)
    Deep discipline knowledge
    • informed and infused by cutting edge research, scaffolded throughout their program of studies
    • acquired from personal interaction with research active educators, from year 1
    • accredited or validated against national or international standards (for relevant programs)
    1-4
    Critical thinking and problem solving
    • steeped in research methods and rigor
    • based on empirical evidence and the scientific approach to knowledge development
    • demonstrated through appropriate and relevant assessment
    2-4
  • Learning Resources
    Required Resources
    There are no prescribed reference texts for this course.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    The course will be primarily delivered through three activities:
    1. Lectures
    2. Practicals
    3. Assignments

    Lectures will introduce and motivate the basic concepts of each topic. Significant discussions and two-way communication are also expected during lectures to enrich the learning experience. Through problem solving and discussions in a small class room setting, particles provide opportunities for obtaining feedback. The assignments will reinforce theoretical concepts by their application to problem solving. . All material covered in the lectures, practices and assignments are assessable.
    Workload

    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. Students are expected to spend 10-12 hours per week on the course. This includes a 2-hour lecture, a 2-hour practical (once every fortnight with online mode and face-to-face mode),  up to 7 hours per week on completing assignments.

    Assignment work will be subjected to deadlines. Students are expected to manage their time effectively to allow timely submission, especially with consideration to the workload of other courses.
    Learning Activities Summary

    No information currently available.

  • 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
    The course includes the following assessment components:
    Two assignments: 50%
    Three quizzes: 15%
    Essay 35%
    Assessment Detail

    No information currently available.

    Submission

    Marks will be capped for late submissions, based on the following schedule:
    1 day late – mark capped at 75%
    2 days late – mark capped at 50%
    3 days late – mark capped at 25%
    more than 3 days late – no marks available.

    Extensions to due dates will only be considered under exceptional medical or personal conditions and will not be granted on the last day due, or retrospectively. Applications for extensions must be made to the course coordinator by e-mail or hard copy and must include supporting documentation – medical certificate or letter from the student counselling service.
    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 (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’s disciplinary procedures.

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