SIT787 - Mathematics for Artificial Intelligence
Unit details
| Year: | 2022 unit information |
|---|---|
| Important Update: | Unit delivery will be in line with the most current COVIDSafe health guidelines. We continue to tailor learning experiences for each unit to achieve the best possible mix of online and on-campus activities that successfully blend our approaches to learning, working and research. Please check your unit sites for announcements and updates. Last updated: 4 March 2022 |
| Enrolment modes: | Trimester 1: Waurn Ponds (Geelong), Online Trimester 2: Waurn Ponds (Geelong), Online |
| Credit point(s): | 1 |
| EFTSL value: | 0.125 |
| Unit Chair: | Trimester 1: Asef Nazari Trimester 2: Asef Nazari |
| Prerequisite: | Nil |
| Corequisite: | Nil |
| Incompatible with: | Nil |
| Typical 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 - campus: | 3 x 1 hour online classes per week, 1 x 1 hour workshop per week. |
| Scheduled learning activities - cloud: | Online independent and collaborative learning including optional scheduled activities as detailed in the unit site. |
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.
| ULO | These are the Learning Outcomes (ULO) for this unit. At the completion of this unit, successful students can: | Deakin Graduate Learning Outcomes |
|---|---|---|
| ULO1 | Explain the role and application of mathematical concepts associate with artificial intelligence. | GLO1: Discipline-specific knowledge and capabilities |
| ULO2 | Identify and summarise mathematical concepts and technique covered in the unit needed to solve mathematical problems from artificial intelligence applications. | GLO1: Discipline-specific knowledge and capabilities |
| ULO3 | Verify and critically evaluate results obtained and communicate results to a range of audiences. | GLO2: Communication |
| ULO4 | Read and interpret mathematical notation and communicate the problem-solving approach used. | GLO1: Discipline-specific knowledge and capabilities |
These Unit Learning Outcomes are applicable for all teaching periods throughout the year
Assessment
| Assessment Description | Student output | Grading and weighting (% total mark for unit) | Indicative due week |
|---|---|---|---|
| Problem Solving Task 1 | Written responses to mathematical problems | 10% | Week 4 |
| Problem Solving Task 2 | Written responses to mathematical problems | 25% | Week 8 |
| Problem Solving Task 3 | Written responses to mathematical problems | 25% | Week 11 |
| Examination | 2-hour written examination | 40% | Examination period |
The assessment due weeks provided may change. The Unit Chair will clarify the exact assessment requirements, including the due date, at the start of the teaching period.
Hurdle requirement
To be eligible to obtain a pass in this unit, students must achieve a mark of at least 50% in the examination.
Learning Resource
The texts and reading list for the unit can be found on the University Library via the link below: SIT787 Note: Select the relevant trimester reading list. Please note that a future teaching period's reading list may not be available until a month prior to the start of that teaching period so you may wish to use the relevant trimester's prior year reading list as a guide only.
Unit Fee Information
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