PHIL 7005 - Machine Learning and Artificial Intelligence

North Terrace Campus - Trimester 3 - 2024

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.

  • General Course Information
    Course Details
    Course Code PHIL 7005
    Course Machine Learning and Artificial Intelligence
    Coordinating Unit Philosophy
    Term Trimester 3
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Contact Up to 3 hours per week
    Available for Study Abroad and Exchange N
    Restrictions This course is available to students enrolled in any relevant Postgraduate Masters degree, subject to approval by the relevant Department.
    Course Description 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 Staff

    Course Coordinator: Dr Philip Gerrans

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes

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

    1. understand the nature of machine learning and its relationship to other forms of computation
    2. relate their understanding to wider issues about the nature of cognition including reasoning, decision making learning and mental representation
    3. assess the strengths weaknesses and prospects of machine learning in a variety of domains
    4. understand the ethical implications of deep learning
    5. 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.

    4
  • 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

    No information currently available.

    Learning Activities Summary

    No information currently available.

  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

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

    Assessment Summary

    No information currently available.

    Assessment Detail

    No information currently available.

    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 Access Adelaide.

  • 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.

  • Student Support
  • Policies & Guidelines
  • 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.

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