SIG787 - Mathematics for Artificial Intelligence
| Year: | 2027 unit information |
|---|---|
| Enrolment modes: | Trimester 3: Great Learning |
| Credit point(s): | 1 |
| EFTSL value: | 0.125 |
| Prerequisite: | Nil |
| Corequisite: | Must be enrolled in S773 Master of Data Science (Global) or S732 Master of Applied Artificial Intelligence (Global) |
| Incompatible with: | SIT787 |
| Study commitment: | Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit. |
| Scheduled learning activities - online: | Online independent and collaborative learning including optional scheduled activities as detailed via the Great Learning platform. |
Content
This unit provides the fundamental mathematical and statistical knowledge to understand important concepts in Artificial Intelligence (AI) and Data Science (DS). The contents of the unit are selected carefully to cover the most frequent mathematical and statistical tools and techniques to help students easily learn technical topics in AI and DS, enabling students to obtain enough experience to expand their knowledge into new directions if required. The unit builds a strong bridge between simple and core mathematical and statistical concepts and advanced techniques that are used in developing modern algorithms in AI and DS.
Hurdle requirements
To be eligible to obtain a pass in this unit, students must achieve a mark of at least 50% in the End-of-Unit Assessment, as well as at least 50% overall (with the End-of-Unit Assessment and assessment tasks combined).