COMP SCI 3007 - Artificial Intelligence
North Terrace Campus - Semester 1 - 2022
General Course Information
Course Code COMP SCI 3007 Course Artificial Intelligence Coordinating Unit School of 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 1103, COMP SCI 1203, COMP SCI 2009, COMP SCI 2103, COMP SCI 2202 or COMP SCI 2202B Assumed Knowledge Mathematics background in probability and statistics as covered in STATS 1000, MATHS 1004, SACE Stage 2 Mathematical Methods, or the online courses (MathTrackX: Probability and MathTrackX: Statistics) 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 OutcomesOn 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)
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.
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.
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.
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.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.
Due to the current COVID-19 situation modified arrangements have been made to assessments to facilitate remote learning and teaching. Assessment details provided here reflect recent updates.
In place of a written final exam for the course, we will be conducting an online exam (likely to be open book).
The current plan is for the online exam to occur either at the exact same date and time scheduled for the original physical exam
(to be released by the exams office later); or at another suitable date and time in the exams period (if the exams
office will not be releasing an exams schedule this semester).
In either case, please make yourself available in the exams period to take the exam.
The current plan is to conduct the online exam via MyUni (though this is subject to the system being properly tested and verified for this purpose), so you need to make sure that you have a good Internet connection to not be disadvantaged during the exam.
Note that the online exam is the only option - we will not be holding a physical exam simultaneously.
All other course work (Assignments 1-3) will proceed as per normal with online submissions.
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.
Assessment DetailSummary 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.
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.
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
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Elder Conservatorium of Music Noise Management Plan
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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