36121 Artificial Intelligence Principles and Applications
Warning: The information on this page is indicative. The subject outline for a
particular session, location and mode of offering is the authoritative source
of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.
Subject handbook information prior to 2025 is available in the Archives.
Credit points: 8 cp
Subject level:
Postgraduate
Result type: Grade, no marksRequisite(s): 48 credit points of completed study in spk(s): C04379 Master of Business Analytics (Extension)
These requisites may not apply to students in certain courses.
There are course requisites for this subject. See access conditions.
Description
Artificial Intelligence (AI) is one of the hottest topics in computer science and engineering in the modern era. It is at the heart of current technology and the applications and understanding of AI helps to solve many contemporary and future real world problems. Artificial intelligence is a research domain and the knowledge of AI helps us to understand the intelligent human behaviors on a computer.
The key objectives of AI are to make a computer in such a way that it can plan, learn, and resolve problems autonomously. Through this subject students get the idea of current research topics in artificial intelligence include reasoning, general problem solving approaches, planning, learning and so on. They learn advanced search techniques in AI for introducing innovative Artificial Intelligence solutions.
This subject helps students to learn and strengthen their technical skills and build their careers in AI based industries. Furthermore, this subject helps students to solve real world complex problems based on AI techniques.
Subject learning objectives (SLOs)
Course intended learning outcomes (CILOs)
This subject contributes specifically to the development of the following course intended learning outcomes:
- Explore and test models and generalisations for describing the behaviour of sociotechnical systems and selecting data sources, taking into account the needs and values of different contexts and stakeholders (1.2)
- Explore, analyse, manipulate, interpret and visualise data using data science techniques, software and technologies to make sense of data rich environments (2.2)
- Understand and deal critically and openly with the uncertainty, ambiguity and complexity associated with people, systems and data (2.3)
- Apply and assess data science concepts, theories, practices and tools for designing and managing data discovery investigations in professional environments that draw upon diverse data sources, including efforts to shed light on underrepresented components (2.4)
- Critically examine the perceived value of data analytics outcomes and clearly articulate implications for different stakeholders and organisations (3.2)
- Engage in active, reflective practice that supports flexible navigation of assumptions, alternatives and uncertainty in professional data science contexts (5.1)
Teaching and learning strategies
Blend of online and face to face activities: The subject is offered through a series of teaching sessions which blend online and face-to-face learning. Students learn through interactive lectures and classroom activities making use of the subject materials on canvas. They also engage in individual and collaborative learning activities to understand and apply text analysis techniques in diverse settings.
Authentic problem based learning: This subject offers a range of authentic data science problems to solve that will help develop students’ analysis skills. They work on real world data analysis problems for broad areas of interest using unstructured data and contemporary techniques.
Collaborative work: Group activities will enable students to leverage peer-learning and demonstrate effective team participation, as well as learning to work in professional teams with an appreciation of diverse perspectives on data science and innovation.
Future-oriented strategies: Students will be exposed to contemporary learning models using speculative thinking, ethical and human-centered approaches as well as reflection. Electronic portfolios will be used to curate, consolidate and provide evidence of learning and development of course outcomes, graduate attributes and professional evolution. Formative feedback will be offered with all assessment activities for successful engagement.
Content (topics)
• General Problem Solving Approaches
• Logic and Structural Knowledge representation
• Bayesian Network and Probabilistic Reasoning
• Deep Neural Network, Multilayer Network and Decision trees
• Real world applications of Artificial Intelligence (AI)
Assessment
Assessment task 1: Review of Artificial intelligence approaches and limitations
| Objective(s): | This assessment task contributes to the development of course intended learning outcome(s): 2.2, 2.3 and 5.1 |
|---|---|
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 35% |
Assessment task 2: Student recent journal study
| Objective(s): | This assessment task contributes to the development of course intended learning outcome(s): 1.2 and 2.2 |
|---|---|
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 25% |
Assessment task 3: Real World Applications of Artificial Intelligence
| Objective(s): | This assessment task contributes to the development of course intended learning outcome(s): 1.2, 2.2, 2.4 and 3.2 |
|---|---|
| Type: | Report |
| Groupwork: | Group, group and individually assessed |
| Weight: | 40% |
Minimum requirements
Students must achieve at least 50% of the subject’s total marks.