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COMP SCI 7314 - Introduction to Statistical Machine Learning

North Terrace Campus - Trimester 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.

  • General Course Information
    Course Details
    Course Code COMP SCI 7314
    Course Introduction to Statistical Machine Learning
    Coordinating Unit Computer Science
    Term Trimester 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 Alfred Krzywicki

    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.
    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.

    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
    This course is delivered in a semester, trimester and intensive format, although enrolment options may be limited by availability.

    This course offers opportunities for you to learn through blended learning approaches, meaning some of the learning is done autonomously online and some of the learning is done through face-to-face engagement. This blended approach is used to create a rich scaffolded and supportive learning experience.

    Workload

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

    This is a 3-unit course. In the semester or trimester format, you are expected to allocate the following study time to fully meet the Course Learning Outcomes (CLOs) for this course. Please note that students work at different paces, so this indicates the approximate time required to complete this course.

    Learning Activity Hours/Week Duration Total
    Online learning activites 1 hour 12  weeks 12 hours
    Face-to-face learning activities 3 hours 12 weeks 36 hours
    Independent study 4 hours 12 weeks 48 hours
    Assessment tasks 5 hours 12 weeks 60 hours
    Expected total student workload 156 hours
    Learning Activities Summary
    You will be required to complete the online learning activities available on MyUni prior to regular face-to-face learning sessions. Throughout these autonomous tasks, you will have time to process new concepts and build foundational knowledge around them. In the face-to-face sessions, you will get a chance to apply that learning to build new skills and address real-world problems.

    Learning activities, both online and face-to-face, are scaffolding to the learning builds throughout the course. Through this learning experience, you will be asked to draw on a range of lower-order and higher-order thinking skills.
  • 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
    Two practical assignments,
    10 weekly quizzes focused on theoretical part of the course
    Final test on the thory and application of Machine Learning.
    Assessment Detail
    Weekly quizzes in weeks 1-10

    Due: each week 1-10
    Percentage of grade: 10%
    Type: Quiz, individual

    Assignment 1: Regression and classification techniques

    Due: Week 4 (tentative)
    Percentage of grade: 15%
    Type: Open-book, individual
    Assessment Documents: Code and report

    Assignment 2: Machine Learning Algorithms and their application

    Due: Week 11 (tentative)
    Percentage of grade: 30%
    Type: Open-book, individual
    Assessment Documents: Code and Report

    Final Test: Machine Learning Algorithms and Applications

    Due: Week 13 (tentative)
    Percentage of grade: 40%
    Type: Open-book, in-class, individual
    Type: Quiz

    Remaining 5% of grade will be awarded for active participation in class sessions and workshops.

    Please note that the above detaila may vary slightly when the course is published.


    Submission
    Unless otherwise specified, submit all of your assessments to the Assignments space in the MyUni course site for this course. For written assessments, your submissions will go through Turnitin to check for originality. Make sure your submissions adhere to the 成人大片 Academic Integrity policies.
    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 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鈥檚 disciplinary procedures.

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