COMP SCI 7059 - Artificial Intelligence
North Terrace Campus - Semester 1 - 2015
General Course Information
Course Code COMP SCI 7059 Course Artificial Intelligence Coordinating Unit School of Computer Science Term Semester 1 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact Up to 2.5 hours per week Available for Study Abroad and Exchange Y Assumed Knowledge COMP SCI 7082 or COMP SCI 7201 Course Description 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.
Course Coordinator: Professor Tat-Jun Chin
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning Outcomes
No information currently available.
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) Knowledge and understanding of the content and techniques of a chosen discipline at advanced levels that are internationally recognised. 1,2,5 The ability to locate, analyse, evaluate and synthesise information from a wide variety of sources in a planned and timely manner. 1,2,5 An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems. 4 A proficiency in the appropriate use of contemporary technologies. 3,4 A commitment to continuous learning and the capacity to maintain intellectual curiosity throughout life. 5
Required ResourcesThere are no prescribed reference texts for this course.
Recommended ResourcesThe 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 LearningAll 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 ModesThe course will be primarily delivered through three activities:
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 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.
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment SummaryThe course includes the following assessment components:
- Final written exam at 55%.
- Three assignments at 15% each.
Assessment Related RequirementsStudents 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.
No information currently available.
SubmissionThe 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.
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.
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.
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangement Policy
- Academic Honesty Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs
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- Coursework Academic Programs Policy
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- Modified Arrangements for Coursework Assessment
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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