COMP SCI 7314 - Statistical Machine Learning

North Terrace Campus - Trimester 2 - 2025

Statistical Machine Learning focuses on algorithms that automatically improve their performance through learning. Examples include computer programs that learn to detect objects in images or videos, predict stock market trends, or rank web pages. This advanced course provides a comprehensive overview of key concepts, widely used techniques, and foundational algorithms in statistical machine learning. It covers core topics such as dimensionality reduction, classification and regression, support vector machines, and deep neural networks, as well as recent developments including Large Language Models (LLMs), Agentic AI, and Causal AI. The course is designed to equip students with both the theoretical foundations, practical skills and intuition behind modern statistical machine learning methods. By the end, students will have a solid understanding of how, why, and when to apply these methods to real-world problems.

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Course Details

Course Code	COMP SCI 7314
Course	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 7211 or COMP SCI 7201 or COMP SCI 7202
Incompatible	COMP SCI 3314, COMP SCI 3314MELB, COMP SCI 7314MELB, COMP SCI 7614
Assumed Knowledge	MATH 7027 and (COMP SCI 7317 or COMP SCI 7327 or MATH 7107) Calculus, Linear Algebra, Probability and Statistics, Python programming, Basics of Machine Learning
Restrictions	Available only to students in 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 and Grad Dip Computer Science, M Comp & Innovation
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 .

Course Learning Outcomes

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

Explain the fundamental concepts of machine learning, including classic algorithms such as Support Vector Machines, Neural Networks, and Deep Learning, as well as recent advancements such as Large Language Models (LLMs), Agentic AI, and Causal AI.
Understand and articulate the core principles and theoretical foundations of machine learning, equipping them with the insight to develop new algorithms in the future.
Implement the machine learning algorithms covered in the course through programming.
Perform mathematical derivations of the algorithms taught in the course.

杏吧直播 Graduate Attributes

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

杏吧直播 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

杏吧直播 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.

Learning Resources

Required Resources

1. No textbook required.

2. Knowledge of basic statistics, probability, linear algebra and calculus is required. Knowledge of optimisation would be helpful,

3. Ability to program in Python 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 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.

The 杏吧直播'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

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

Authorised by:	Deputy Vice-Chancellor and Vice-President (Academic)
Maintained by:	Course Outlines Administrator

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