PHIL 7005 - Machine Learning and Artificial Intelligence

North Terrace Campus - Semester 2 - 2023

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.

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Course Details

Course Code	PHIL 7005
Course	Machine Learning and Artificial Intelligence
Coordinating Unit	Philosophy
Term	Semester 2
Level	Postgraduate Coursework
Location/s	North Terrace Campus
Units	3
Contact	Up to 3 hours per week
Available for Study Abroad and Exchange	N
Incompatible	PHIL 7005OL
Restrictions	This course is available to students enrolled in any relevant Postgraduate Masters degree, subject to approval by the relevant Department.
Assessment	Online assignments 50%, Essay/Assignments 50%

Course Staff

Course Coordinator: Dr Philip Gerrans

Course Timetable

The full timetable of all activities for this course can be accessed from .

Course Learning Outcomes

On successful completion of this course students will be able to:

understand the nature of machine learning and its relationship to other forms of computation
relate their understanding to wider issues about the nature of cognition including reasoning, decision making learning and mental representation
assess the strengths weaknesses and prospects of machine learning in a variety of domains
understand the ethical implications of deep learning
Express their understanding in a variety of written forms

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最新糖心Vlog Graduate Attribute	Course Learning Outcome(s)
Attribute 1: Deep discipline knowledge and intellectual breadth Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.	1,2,3,4
Attribute 2: Creative and critical thinking, and problem solving Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.	2,3
Attribute 3: Teamwork and communication skills Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.	5
Attribute 5: Intercultural and ethical competency Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.	4

Learning & Teaching Modes

No information currently available.

Workload

The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

WORKLOAD	TOTAL HOURS
STRUCTURED LEARNING
1 x 3 hour seminar per week	36 hours per semester
Sub-total	36 hours
SELF-DIRECTED LEARNING
4 hours reading per week	48 hours per semester
3 hours research and independent reading per week	36 hours per semester
3 hours assignment preparation per week	36 hours per semester
Sub-total	120 hours
TOTAL	156 hours

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Learning Activities Summary

The course is divided into 3 units of roughly equal size:

UNIT	LECTURE TOPIC
1	Computation, Cognition, and Artificial Intelligence
2	Approaches to Learning from Experience
3	Ethical issues in Machine Learning and AI

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The 最新糖心Vlog's policy on Assessment for Coursework Programs is based on the following four principles:

Assessment must encourage and reinforce learning.
Assessment must enable robust and fair judgements about student performance.
Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
Assessment must maintain academic standards.

Assessment Summary

ASSESSMENT TASK	TASK TYPE	WEIGHTING	COURSE LEARNING OUTCOME(S)
Final Essay	Summative	50%	1,2,3,4,5
Shorter essay	Formative and Summative	30%	1,2,3,4,5
Online assignments	Formative and Summative	20%	1,2,4,5

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Assessment Detail

Assessment	Description	% weighting
Final Essay	An extended final essay of up to 2500 words due after the end of semester on topics drawn from the whole course.	50%
Shorter essay	A short essay of up to 1200 words due in mid-semester.	30%
Online assignments	A variety of short writing tasks totalling 1000 words in preparation for seminar discussion.	20%

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Submission

No information currently available.

Course Grading

Grades for your performance in this course will be awarded in accordance with the following scheme:

M10 (Coursework Mark Scheme)
Grade	Mark	Description
FNS		Fail No Submission
F	1-49	Fail
P	50-64	Pass
C	65-74	Credit
D	75-84	Distinction
HD	85-100	High Distinction
CN		Continuing
NFE		No Formal Examination
RP		Result Pending

Further details of the grades/results can be obtained from Examinations.

Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

Final results for this course will be made available through .

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Authorised by:	Deputy Vice-Chancellor and Vice-President (Academic)
Maintained by:	Course Outlines Administrator

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