COMP SCI 7327 - Concepts in Artificial Intelligence and Machine Learning

North Terrace Campus - Trimester 1 - 2023

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 COMP SCI 7327
    Course Concepts in Artificial Intelligence and Machine Learning
    Coordinating Unit Computer Science
    Term Trimester 1
    Level Postgraduate Coursework
    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
    Assessment Practical exercises, workshops, case studies and/or final exam.
    Course Staff

    Course Coordinator: Dr Muhammad Uzair

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    1. Develop a broad understanding of machine learning (ML) and artificial intelligence (AI) concepts.
    2. Acquire knowledge about commonly used ML and AI algorithms and their applications.
    3. Gain proficiency in Python for basic ML and AI tasks.
    4. Apply ethical norms to AI and machine learning, considering ethics, privacy, and security
    5. Effectively communicate AI and ML concepts in both written and oral forms.
    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.

    2, 3

    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 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.


    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
    All required resources for this course will be provided online via the MyUni platform.
    Recommended Resources
    1. For general AI: David L. Poole and Alan K. Mackworth: "Artificial Intelligence: Foundations of Computational Agents" 2nd Edition
    2. For Machine Learning: Rebala, G., Ravi, A., & Churiwala, S. (2019). "An introduction to machine learning". Springer.
    Both books are available online.
    Online Learning

    For online learning, all required resources for this course will be accessible through the MyUni platform. This includes live streaming of lectures as well as later availability of recorded lectures.

  • Learning & Teaching Activities
    Learning & Teaching Modes
    The course will primarily utilize three activities to deliver the content:

    Lectures: Lectures will introduce and explain the fundamental concepts of each topic. Interactive discussions will be encouraged to enhance the learning experience..

    Practical Sessions: Practical sessions will focus on the practical implementation and application of the knowledge gained from the lectures. Students will learn how to implement and apply the concepts to real-world problems through programming work.

    Assignments: Assignments will serve to reinforce the concepts learned by providing opportunities for problem-solving.

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

    Students are expected to allocate 8-10 hours per week for this course. Contact time for teaching and learning activities will be around 2-4 hours, while the remaining 6-8 hours will be spent on individual and group work to complete the required learning tasks and activities, fostering a deeper understanding of the course content.

    Assigmment work will be subjected to deadlines. Students are expected to manage their time effectively to allow timely submission, especially with consideration to workload of other courses.
    Learning Activities Summary
    Students are strongly encouraged to attend lectures as they provide additional content beyond what is available on the slides such as in-depth explaination, clarification of conepts, engatement and interaction. Active participation and questioning during lectures are also encouraged. Lecture contencts including slides and other learning materials such as readings, textbooks, supplementary resources, and relevant articles will be available on MyUni page of the course.

    This is a 4-unit course, requiring approximately 8 hours of weekly commitment. This includes a 2-hour lecture session, 2 hours for practical session, and up to 4 hours per week for learning and completing assignments.

    Assignments will have specific deadlines. Students are expected to manage their time effectively to ensure timely submission, taking into account the workload of other courses.
    Specific Course Requirements
    A foundational understanding of mathematics concepts from linear algebra, statistics, probability, and optimization would be advantageous for the course, although it is not mandatory. The relevant topics will be covered as needed throughout the course.

    Python programming ability is required but not mandatory, as practical sessions will be provided to cover the necessary 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
    The course will consist of four coding assignments that involve solving AI and ML problems using Python programming language and ML libraries, in addition to a presentation or report requirement.
    Assessment Related Requirements
    Assessements require using Python programming language and ML libraries, in addition to a presentation or report requirement.
    Assessment Detail
    Assignment 1
    Percentage of grade: 20%
    Type: Open-book
    Assessment Documents: Code and Report

    Assignment 2
    Percentage of grade: 25%
    Type: Open-book
    Assessment Documents: Code and Report

    Assignment 3
    Percentage of grade: 30%
    Type: Open-book
    Assessment Documents: Code and Report

    Assignment 4
    Percentage of grade: 25%
    Type: Open-book
    Assessment Documents: Technical presentation of a concept in AI and ML
    Submissions will be done online via MyUni.
    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

    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.