| Type: | Postgraduate Subject |
|---|---|
| Code: | ENAI71-102 |
| EFTSL: | 0.125 |
| Faculty: | Bond Business School |
| Credit: | 10 |
| Study areas: |
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| Subject fees: |
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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
Learning outcomes
- Apply advanced data structures such as arrays, stacks, queues, trees, and graphs to solve complex computational problems.
- Critically assess the time and space complexity of algorithms using asymptotic notation to evaluate their efficiency, scalability and utility.
- Implement advanced sorting and searching algorithms using an appropriate programming language and optimise for performance.
- Evaluate algorithmic design paradigms, such as divide-and-conquer, greedy algorithms, and dynamic programming to develop advanced programming solutions.
- Critically evaluate AI tools for their use in the design, implementation, and refinement of algorithms to identify their limitations and risks.
- Communicate algorithmic solutions to technical and non-technical audiences using diagrams, pseudo-code and computational analysis to show how proposed solutions address complex business problems.
Enrolment requirements
| Requisites: |
Nil |
|---|---|
| Restrictions: |
Nil |