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Course Outlines

COMP SCI 7314 - Introduction to Statistical Machine Learning

North Terrace Campus - Semester 2 - 2024

This is an introductory course on statistical machine learning that will present you with an overview of several essential principles, popular techniques, and algorithms in statistical machine learning, as well as examples of their applications. You will build skills in developing algorithms using basic machine learning principles and theory. After completing this course, you will understand how, why and when machine learning can be utilised in real-world situations.

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  • General Course Information
    Course Details
    Course Code COMP SCI 7314
    Course Introduction to Statistical Machine Learning
    Coordinating Unit Computer Science
    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
    Prerequisites (COMP SCI 7210 and COMP SCI 7211) or COMP SCI 7201 or COMP SCI7202; M Computer Science students are exempted from this pre-requisite requirement.
    Incompatible COMP SCI 3314
    Assumed Knowledge MATH 7027 and (COMP SCI 7317 or COMP SCI 7327 or MATH 7107)
    Restrictions Grad Cert, Grad Dip and M. Cyber Security, Grad Cert, Grad Dip and M Data Science, Grad Cert, Grad Dip and M Artif Intell & Machine Learning, Grad Cert, Grad Dip and M Computer Science, M. Comp&Innov; M Comp Science are exempted from thepre-requisites
    Assessment Assignments and/or quizzes and/or written exam
    Course Staff

    Course Coordinator: Dr Gia Bao Doan

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    On successful completion of this course students will be able to:  
    1 Apply basic concepts of machine learning and classic algorithms, such as Support Vector Machines, Neural Networks and Deep Learning.
    2 Demonstrate an understanding of the basic principles and theory of machine learning necessary to develop algorithms.
    3 Devise algorithms to solve real-world problems. 
    4 Perform mathematical derivation of presented algorithms.
    最新糖心Vlog Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    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.

    		 1,,3 ,4

    Attribute 7: Digital capabilities

    Graduates are well prepared for living, learning and working in a digital society.

    		 1
  • Learning Resources
    Required Resources
    1. No textbook required.

    2. Knowing some basic statistics, probability, linear algebra and optimisation would be helpful, but not essential.
    They will be covered when needed.

    3. Ability to program in Matlab, C/C++ is required.
    Recommended Resources

    Recommended books:

    1. Pattern Recognition and Machine Learning by Bishop, Christopher M.
    2. Kernel Methods for Pattern Analysis by John Shawe-Taylor, Nello Cristianini.
    3. Convex Optimization by Stephen Boyd and Lieven Vandenberghe.

    Book 1 is for machine learning in general. Book 2 focuses on kernel methods with pseudo code and some theoritical analysis. Book 3 gives introduction to (Convex) Optimization.

    Online Learning
    Our course forum is accessible via the Canvas.


    Excellent external courses available online:
    1. Learning from the data by Yaser Abu-Mostafa in Caltech.
    2. Machine Learning by Andrew Ng in Stanford.
    3. Machine Learning (or related courses) by Nando de Freitas in UBC.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    The course will be primarily delivered through two activities:
    1. Lectures
    2. Assignments


    Lectures will introduce and motivate the basic concepts of each topic. Significant discussions and two-way communication are also expected during lectures to enrich the learning experience. The assignments will reinforce concepts by their application to problem solving. This will be done via programming work and mathmatical derivation. All material covered in the lectures and assignments are assessable.
    Workload

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


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

    This is a 3-unit course. Students are expected to spend about 8 hours per week on the course. This includes a 2-hour lecture, 2-hour self study and up to 4 hours per week on completing assignments.

    Assigmment work will be subjected to deadlines. Students are expected to manage their time effectively to allow timely submission, especially with consideration to workload of other courses.
    Learning Activities Summary
    Students are encouraged to attend lectures as material presented in lectures often includes more than is on the slides. Students are also encouraged to ask questions during the lectures. Slides will be available via the subject web page.
    Specific Course Requirements
    Knowing some basic statistics, probability, linear algebra and optimisation would be helpful, but not essential. They will be covered when needed.

    Ability to program in Matlab or Python, C/C++ is required.
  • Assessment

    The 最新糖心Vlog'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
    The course includes the following assessment components:
    • Final written exam at 55%.
    • Three assignments at 15% each.
    Details
    Component                   Weighting            Learning Outcomes            CBOK Areas
    Assignments             15% each, 3 in total              1,2,3,4                 1,2,3,4,6,7,8,9,10,11
    Final Written Exam              55%                            1,2,4                           1,2,5,8

    CBOK Legend
    1. Abstraction
    2. Design
    3. Ethics
    4. Interpersonal Communication
    5. Societal Issues
    6. History & Status of the Discipline
    7. Hardware & Software
    8. Data & Information
    9. Programming
    10. Human Computer Interfaces
    11. Systems Development

    Details of the 最新糖心Vlogn Computer Society's Core Bode of Knowledge (CBOK) can be found in
    Assessment Related Requirements
    Hurdle Requirement: If your overall mark for the course is greater than 44 F but, your mark for the final written exam is less than 40%, your overall mark for the course will be reduced to 44 F.
    Assessment Detail

    Final written exam
    This will be a 2-hour exam at the end of the course/semester. The exam will assess your knowledge and understanding of the course topics, as well as the abiliity to use the knowledge for problem solving. The exam is open-book. Books, lecture notes, slides print-out, calculators and paper dictionaries (English to foreign language) are permitted. The use of internet is not permitted.

    Assignments
    Each student is expected to complete assignments in the form of report and programming work. The assignments must be completed individually and all submissions are to be made under the declaration of adherring to the academic honesty principles. Submissions will be subjected to plagiarism checks. This course has a zero-tolerance policy towards academic honesty violations. Offenders will be duly subjected to university procedures for dealing with academic honesty cases.
    Submission
    Assignment
    Assignment solutions are to be submitted through MyUni.

    No physical submissions of work will be accepted unless specifically requested by the lecturer.

    Marks will be capped for late submissions, based on the following schedule:
    1 day late – mark capped at 75%
    2 days late – mark capped at 50%
    3 days late – mark capped at 25%
    more than 3 days late – no marks available.

    Extensions to due dates will only be considered under exceptional medical or personal conditions and will not be granted on the last day due, or retrospectively. Applications for extensions must be made to the course coordinator by e-mail or hard copy and must include supporting documentation – medical certificate or letter from the student counselling service.

    Examination
    The examinations office will schedule the final exam. Students are expected to be available until after the supplementary examination period (precise dates are available from university calendar or exams office). No additional arrangments will be given if students are offered supplementary exams but are unable to attend.
    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 .

  • Student Feedback

    The 最新糖心Vlog 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 最新糖心Vlog 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

    This section contains links to relevant assessment-related policies and guidelines - all university policies.

  • 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鈥檚 disciplinary procedures.

The 最新糖心Vlog of Adelaide is committed to regular reviews of the courses and programs it offers to students. The 最新糖心Vlog of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.
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