Hobart, Launceston
Introduction
This unit is designed to give students an insight into a range of Artificial Intelligence (AI) techniques. AI is an emerging branch of Information and Communication Technology which has created an array of disruptions in multiple industries. The AI techniques leverage the computational power of machines to deal with complex tasks which normally require human intelligence. You will learn the two main streams of AI including Knowledge-Driven AI and Data-Driven AI as well as current advanced AI techniques which have been used by giant tech companies like Google, Amazon, IBM and Microsoft.
With the technologies discussed in the lecture, it brings together the state-of-the-art research and practical techniques in AI, providing students with the knowledge and capacity to conduct AI research and to develop AI applications. Students will have a chance to master advanced AI tools and APIs to explore and specialise his/her understanding, and also be required to use these technologies to develop a system for a AI application.
At the end of this unit, a student should understand the fundamental AI technologies and be able to provide design recommendations for a particular AI application.
Summary 2021
| Unit name | Introduction to Artificial Intelligence |
|---|---|
| Unit code | KIT509 |
| Credit points | 12.5 |
| Faculty/School | College of Sciences and Engineering School of Information and Communication Technology |
| Discipline | Information & Communication Technology |
| Coordinator | Wenli Yang |
| Level | Postgraduate |
| Available as student elective? | Yes |
| Breadth Unit? | No |
Availability
Note
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* The Final WW Date is the final date from which you can withdraw from the unit without academic penalty, however you will still incur a financial liability (see withdrawal dates explained for more information).
Learning Outcomes
1 | Explain the fundamental concepts of Artificial Intelligence, its capabilities and limitations |
2 | Apply suitable AI techniques to solve theoretical and practical problems |
3 | Create AI applications using AI development tools |
Fees
Teaching
| Teaching Pattern | 1 x 60 min lecture weekly, 1 x 120 min workshop weekly |
|---|---|
| Assessment | AT1 - test (20%) AT2 - test (20%) AT3 - assignment (30%) AT4 - 2-hour exam (30%) |
| Timetable | View the lecture timetable | View the full unit timetable |
Textbooks
| Required |
|---|
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