ENAI11-102: Algorithms and Data Structures with AI-Assisted Implementation

Description

This subject introduces students to the core principles of algorithms and data structures, equipping them with the skills to design efficient solutions to computational problems. The subject covers fundamental data structures such as arrays, stacks, queues, trees, and graphs. It also covers classic algorithms for sorting and searching, and their time and space complexity are discussed. Key algorithmic paradigms, including divide and conquer, greedy methods, and dynamic programming, are introduced. AI tools are integrated in the learning, supporting the development process and code generation, debugging, and optimisation of code. Through hands-on programming and reflective practice, students will gain technical proficiency and an understanding of how AI can enhance coding practice.

Subject details

Type: Undergraduate Subject
Code: ENAI11-102
Faculty: Bond Business School
Credit: 10
Study areas:
  • Business, Commerce, and Entrepreneurship

Learning outcomes

  1. Describe and apply fundamental data structures such as arrays, stacks, queues, trees, and graphs to solve computational problems.
  2. Analyse the time and space complexity of algorithms using Big-O notation to evaluate their efficiency.
  3. Implement common sorting and searching algorithms using a real programming language.
  4. Apply algorithm design paradigms such as divide and conquer, greedy algorithms, and dynamic programming to develop programming solutions.
  5. Use AI tools effectively to assist in the design, implementation, and refinement of algorithms, while reflecting on their limitations and appropriate use in programming tasks.

Enrolment requirements

Requisites:

Nil
Assumed knowledge:

Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge.

Assumed Prior Learning (or equivalent):

Restrictions:

Nil

Subject outlines