COMP SCI 7059 - Artificial Intelligence

North Terrace Campus - Semester 1 - 2015

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.

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Course Details

Course Code	COMP SCI 7059
Course	Artificial Intelligence
Coordinating Unit	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
Assessment	written exam, assignments

Course Staff

Course Coordinator: Professor Tat-Jun Chin

Course Timetable

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

Course Learning Outcomes

No information currently available.

最新糖心Vlog 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

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.

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 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 最新糖心Vlog'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 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

No information currently available.

Submission

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 .

Student Feedback

The 最新糖心Vlog 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 最新糖心Vlog 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
This section contains links to relevant assessment-related policies and guidelines - all university policies.
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 最新糖心Vlog of Adelaide is committed to regular reviews of the courses and programs it offers to students. The 最新糖心Vlog of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.

Authorised by:	Deputy Vice-Chancellor and Vice-President (Academic)
Maintained by:	Course Outlines Administrator

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