COMP SCI 1400 - Artificial Intelligence Technologies
North Terrace Campus - Semester 2 - 2024
General Course Information
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 Course Description This course will provide students with introductory knowledge and skills in the application of modern AI tools and techniques. The course introduces Python, a key language for developing modern AI applications. The course then demonstrates how to run, modify and build Python implementations of current AI technologies including, standard and new machine learning and deep learning tools. The course will have a strong emphasis how to best make use of the large range of materials, and tutorials that are released with new AI frameworks. In particular, the course will develop a high-level understanding of the key concepts and terminology allowing students to make use of new frameworks as they emerge. Assessment can include practical exercises, workshops, case studies and a final exam.
Course Coordinator: Yuankai Qi
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.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.
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.
Learning & Teaching Activities
Learning & Teaching ModesThe course will be primarily delivered through three activities:
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.
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
No information currently available.
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:
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.
No information currently available.
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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