COMP SCI 1400 - Artificial Intelligence Technologies
North Terrace Campus - Semester 2 - 2024
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General Course Information
Course Details
Course Code COMP SCI 1400 Course Artificial Intelligence Technologies Coordinating Unit Computer Science Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 4 hours per week Available for Study Abroad and Exchange N Incompatible TECH 1004, TECH 1004UAC Restrictions Not available to BCompSci, B MathsCompSci, BCompSci(Adv) and BE(Hons)(Soft) students Assessment Practical exercises, workshops, case studies and a final exam. Course Staff
Course Coordinator: Dr Kamal Mammadov
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
On successful completion of this course students will be able to:
1 Select, run, modify and build standard Python programs to solve relevant problems using AI or machine learning 2 Identify and use a broad range of existing resources in the development of Ai and machine learning programs 3 Explain key concepts, differences, limitations and opportunities of various AI and machine learning approaches 4 Applying norms to the use of AI and machine learning including considerations of ethics, privacy and security 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.
1-4 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.
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Learning Resources
Required Resources
There are no prescribed reference texts for this course. -
Learning & Teaching Activities
Learning & Teaching Modes
The course will be primarily delivered through three activities:
1. Lectures
2. Practicals
3. 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, particles provide opportunities for obtaining feedback. The assignments will reinforce theoretical concepts by their application to problem solving. . All material covered in the lectures, practices 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 2-hour practical (once every fortnight with online mode and face-to-face mode), up to 7 hours per week on completing assignments.
Assignment work will be subjected to deadlines. Students are expected to manage their time effectively to allow timely submission, especially with consideration to the workload of other courses.Learning Activities Summary
Students are encouraged to attend lectures as material presented in lectures often includes more than is on the slides. Students are also encouraged to ask questions during the lectures. Slides will be available via the subject web page.Specific Course Requirements
Basic knowledge in Python, linear algebra and optimisation would be helpful, but not essential. They will be covered when needed. -
Assessment
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 Summary
The course includes the following assessment components:
Two coding related assignments: 50%
Three quizzes: 15%
Essay 35% . We have hurdle on this assessment. You need to reach at least 50% of this assessment to get pass for the overall course.Assessment Related Requirements
Hurdle Requirement: We have a hurdle on the Essay. You need to reach at least 50% on the Essay to pass the overall course.Assessment Detail
Quizzes (15%). The first quiz is a ungraded survey that helps teaching team to make adjustment for the later content. The second and third quiz are about the content taught in the lecture.
Assignments (50%). The first assignment (25%) will be about machine learning and some concepts. The second assignment (25%) is a project-based one.
Essay (35%). Essay has two submissions: the outline submission (5%) is to ensure correct template is used and structure is correct. The final essay submission is for the full essay. Essay is about ethics in AI.Submission
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 .
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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 (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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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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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 成人大片 is committed to regular reviews of the courses and programs it offers to students. The 成人大片 therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.
