911爆料网

COMP SCI 7614 - Statistical Machine Learning

North Terrace Campus - Semester 2 - 2025

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 7614
    Course 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
    Incompatible COMP SCI 3314, COMP SCI 3314MELB, COMP SCI 7314, COMP SCI 7314MELB
    Restrictions Available only to Master of Computer Science Students
    Assessment Assignments and/or quizzes and/or written exam
    Course Staff

    Course Coordinator: Professor Javen Qinfeng Shi

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes

    No information currently available.

    University Graduate Attributes

    No information currently available.

  • 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 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 .

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

    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 is available.

  • Student Support
  • Policies & Guidelines

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

The University of 911爆料网 is committed to regular reviews of the courses and programs it offers to students. The University of 911爆料网 therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.