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School of Computing · ANU College of Systems and Society
This course teaches introductory programming, fundamental programming language and computer science concepts, and computational problem solving illustrated with applications common in science and engineering, such as data analysis, visualisation, and image processing. We will take a different approach, including prompting and working with GenAI, with emphasis on testing, debugging and problem decomposition, and using GenAI to help understand code. These skills will empower students to write software with the aid of GenAI. Assessment will include exam(s) without the aid of GenAI. Students will also create larger software projects with the aid of GenAI. The course does not require any prior knowledge of programming or computer science. It is designed to be a stand-alone computing unit. Students who wish to study computing courses at a higher level will need to complete COMP1100 and/or COMP1110 in addition to COMP1730.
Homework, Assignments and Lab Tests
Learning outcomes 1, 2, 3, 4, 5, 6
Programming Project
Learning outcomes 1, 2, 3, 4, 5, 6
Final Exam and Mid-semester Test
Learning outcomes 1, 2, 3, 4, 5, 6
Lectures, tutorials, assignments, self-study & exam prep for a total of 130 hours. Other Information Course Webpage This is an introductory course to computing and programming with Python. It teaches programming using Large Language Models as part of programming. This approach is currently a topic of research in learning for computing, being investigated at a small number of Universities worldwide. Current approaches to AI-assisted programming, and LLMs generally, are still being actively researched. We will teach students how to effectively use tools that are the topic of ongoing research into their programming work-flow. Students will gain an appreciation of the strengths and weaknesses of current LLM models in the context of AI-assisted programming.
There are no prescribed texts. Preliminary Reading - Leo Porter and Daniel Zingaro, Learn AI-Assisted Python Programming, Second Edition, Published by Manning, Distributed by Simon & Schuster. - Downey, A., Wentworth, P., Elkner, J., & Meyers, C. (2016). How to think like a computer scientist: learning with python 3. - Sundnes, J. (2020). Introduction to scientific programming with Python (p. 148). Springer Nature.