COMP SCI 7314 - Introduction to Statistical Machine Learning
North Terrace Campus - Trimester 2 - 2023
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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 Trimester 2 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact Up to 2 hours per week Available for Study Abroad and Exchange N Prerequisites COMP SCI 7210 & COMP SCI 7211 Assumed Knowledge MATH 7027, COMP SCI 7317 or COMP SCI 7327 or MATH 7107 Restrictions Students enrolled in the Graduate Certificate, Graduate Diploma and Master of Cyber Security, Master of Data Science or Master of Artificial Intelligence and Machine Learning. 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 .
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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.
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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:
- 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
Assessment Task Weighting Individual/Group Week Due Course Learning Outcome Continuing Assessment Weekly Quizzes 30% Individual 1-12 1, 2, 4 Support Vector Machines 35% Individual 6 1, 2, 3 PCA, Kmeans & Kernel Methods 35% Individual 10 1, 2, 3 Assessment Detail
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 .
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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.
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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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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 成人大片 is committed to regular reviews of the courses and programs it offers to students. The 成人大片 therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.
