COMP SCI 3007 - Artificial Intelligence

North Terrace Campus - Semester 1 - 2019

This is an introductory course on Artificial Intelligence. The topics may include: AI methodology and fundamentals; intelligent agents; search algorithms; game playing; supervised and unsupervised learning; decision tree learning; neural networks; nearest neighbour methods; dimensionality reduction; clustering; kernel machines; support vector machines; uncertainty and probability theory; probabilistic reasoning in AI; Bayesian networks; statistical learning; fuzzy logic. Several assignments will be given to enable the student to gain practical experience in using these techniques.

  • General Course Information
    Course Details
    Course Code COMP SCI 3007
    Course Artificial Intelligence
    Coordinating Unit Computer Science
    Term Semester 1
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 2.5 hours per week
    Available for Study Abroad and Exchange Y
    Prerequisites One of COMP SCI 1007, COMP SCI 1009, COMP SCI 1103, COMP SCI 1203, COMP SCI 2103, COMP SCI 2202 or COMP SCI 2202B
    Assumed Knowledge COMP SCI 2201
    Assessment Written exam and/or assignments
    Course Staff

    Course Coordinator: Professor Ian Reid

    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 Explain what constitutes "Artificial" Intelligence and how to identify systems with Artificial Intelligence.
    2 Explain how Artificial Intelligence enables capabilities that are beyond conventional technology, for example, chess-playing computers, self-driving cars, robotic vacuum cleaners.
    3 Use classical Artificial Intelligence techniques, such as search algorithms, minimax algorithm, neural networks, tracking, robot localisation.
    4 Ability to apply Artificial Intelligence techniques for problem solving.
    5 Explain the limitations of current Artificial Intelligence techniques.

    The above course learning outcomes are aligned with the Engineers Australia Stage 1 Competency Standard for the Professional Engineer.
    The course is designed to develop the following Elements of Competency: 1.1   1.2   1.3   1.4   1.5   1.6   2.1   2.2   2.3   2.4   3.1   3.2   3.3   

    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)
    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
  • Learning Resources
    Required Resources
    There are no prescribed reference texts for this course.
    Recommended Resources
    The recommended textbook for this course is:
    S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. 3rd edition. Prentice Hall.

    Available from Unibooks. The library also has limited copies.
    Online Learning
    All course material including lecture sildes, tutorial sheets, assignment instructions, and lecture recordings, are available from the course homepage:

    The course forum is accessible via:

  • Learning & Teaching Activities
    Learning & Teaching Modes
    The course will be primarily delivered through three activities:
    • Lectures
    • Tutorials
    • 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, tutorials provide opportunities for obtaining feedback. The assignments will reinforce theoretical concepts by their application to problem solving. This will be done via programming work. All material covered in the lectures, tutorials and assignments are assessable.

    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 1-hour tutorial (once every fortnight), 1-2 hours of self preparation prior to tutorials and up to 7 hours per week on completing assignments.

    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
    Section Description
    Game playing Discusses the theory and algorithms behind computer programs that can play games, such as chess and sudoku.
    Machine learning Disccuses algorithms that can automatically adapt to changing environments by learning from past observations. Also includes the theory behind probabilistic reasoning systems.
    Robotics Discusses the basic principles behind robotic systems, especially automatic tracking and navigation.
  • 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:
    • Final written exam at 55%.
    • Three assignments at 15% each.
    Students enrolled at the postgraduate level (COMP SCI 7059) will be required to perform additional assignment work to reflect higher expectations in self learning and critical thinking. Please consult the actual assignment spcifications for details.
    Assessment Related Requirements
    Students must obtain at least 50% of the overall marks to pass the course.

    The final exam component at 55% weighting attracts the minimum performance hurdle. This means that students must obtain at least 40% of the marks for the final exam in order to pass the course.
    Assessment Detail
    Summary of assessment components:
    • Final written exam - this will be a 2-hour exam at the end of the course/semester. The exam will assess your knowledge and understanding of the course topics, as well as the abiliity to use the knowledge for problem solving. The exam is closed-book and calculators are not allowed. Only paper dictionaries (English to foreign language) are permitted.
    • Assignments - each student is expected to complete practical assignments in the form of programming work. The assignments must be completed individually and all submissions are to be made under the declaration of adherring to the academic honesty principles. Submissions will be subjected to plagiarism checks.
    Mapping of assessment components to learning objectives and ICT Core Body of Knowledge (CBOK) skillsets:
    Assessment Type Due Learning objectives Abstraction (CBOK) Design (CBOK) Ethics (CBOK) Communications (CBOK)
    Assignment 1 Summative Approx. week 4 3, 4, 5 3 3 3
    Assignment 2 Summative Approx. week 9 3, 4, 5 3 3 3
    Assignment 3 Summative Approx. week 14 3, 4, 5 3 3 3
    Final exam Summative Exam period 1, 2, 3, 4, 5 4, 5, 6

    This course has a zero-tolerance policy towards academic honesty violations. Offenders will be duly subjected to university procedures for dealing with academic honesty cases.

    The examinations office will schedule the final exam. Students are expected to be available until after the supplementary examination period (precise dates are available from university calendar or exams office). No additional arrangments will be given if students are offered supplementary exams but are unable to attend.

    Assignment solutions are to be submitted through the School of Computer Science's Web Submission System (Websub). Students are also expected to use the school's SVN repository system to store intermediate solutions for the assignments, prior to submitting via Websub. However, any work on SVN which is not submitted via Websub before the deadline will not be marked. Precise instructions are available in the assignment specifications.

    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 ( 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.