PHIL 7005 - Machine Learning and Artificial Intelligence
North Terrace Campus - Semester 2 - 2022
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General Course Information
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
No information currently available.
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
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
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University 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.
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Learning Resources
Required Resources
Required texts will be made available through MyUni in due course. -
Learning & Teaching Activities
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 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 -
Assessment
The University'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 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% 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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Student Feedback
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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Fraud Awareness
Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.
The ³ÉÈË´óƬ is committed to regular reviews of the courses and programs it offers to students. The ³ÉÈË´óƬ therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.
