PHIL 7005 - Machine Learning and Artificial Intelligence
Career: | Postgraduate Coursework |
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Units: | 3 |
Term: | 4239 |
Campus: | North Terrace |
Contact: | Up to 3 hours per week |
Restriction: | This course is available to students enrolled in any relevant Postgraduate Masters degree, subject to approval by the relevant Department. |
Available for Study Abroad and Exchange: | No |
Available for Non-Award Study: | No |
Assessment: | Online assignments 50%, Essay/Assignments 50% |
Syllabus: |
Spectacular advances in Artificial Intelligence (AI) are the result of applying techniques of Deep Learning in Artificial Neural Networks (DLANNs) to a host of problems (face and speech recognition, data collection and customization, translation, navigation, conversation, industrial production, child and aged care) intractable to previous generations of computational systems. So much so that some have predicted the replacement of human by superior artificial intelligence in many domains with catastrophic results. Other argue that interface with DLANNs is already changing the nature of human cognition by enabling the harvesting and deployment of massive amounts of data by algorithms whose operations are opaque to everyday understanding. Deep Learning systems raise a series of related questions about the nature of intelligence and reasoning, bounded rationality, learning, ethical reasoning, emotion, human sociality and, ultimately, cognition itself. This course looks at those issues in depth. It is suitable for computer scientists interested in contextualizing their work in a wider theoretical and practical framework and others interested acquiring a deeper understanding of Machine Learning. No knowledge of coding or relevant mathematics is assumed. Topics covered may include the nature of representation in Deep Learning networks (compositional and hierarchical) and differences between DLANNs and other forms of computation; ethical decision making by humans and DLANNs; empathy and emotion in human/AI interactions; the nature of reinforcement learning; Bayesian reasoning; the role of emotion in deliberation; how contextual information is represented in human and artificial systems; Noam Chomsky versus Deep Learning. Chomsky, a founder of the “cognitive revolution” remains a sceptic arguing that DLANNS represent a quantitative ( exponentially more and faster) not a qualitative improvement in cognition. Students from any background should come away with a deeper understanding not only of DLANNs but of the nature of thought itself. |
Course Fees
Study Abroad student tuition fees are available here
Only some Postgraduate Coursework programs are available as Commonwealth Supported. Please check your program for specific fee information.
The fees displayed below for international students are for students commencing a program in 2024 only. International students who commenced a program in 2023 or prior can find their fee here.
EFTSL | |||
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0.125 |
Course Outline
A Course Outline which includes Learning Outcomes, Learning Resources, Learning & Teaching for this course may be accessed here
Critical Dates
Term | Last Day to Add Online | Census Date | Last Day to WNF | Last Day to WF |
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4239 | Mon 19/09/2022 | Wed 21/09/2022 | Fri 28/10/2022 | Wed 23/11/2022 |
Class Details
Enrolment Class: Seminar | |||||||
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Class Nbr | Section | Size | Available | Dates | Days | Time | Location |
36413 | SE01 | 60 | 39 | 5 Sep - 12 Sep | Monday | 3pm - 5pm | Ingkarni Wardli, 232, Teaching Room |
19 Sep - 21 Nov | Monday | 3pm - 5pm | Engineering Nth, N132, Teaching Room |