COMP SCI 3314 - Introduction to Statistical Machine Learning
North Terrace Campus - Semester 2 - 2021
General Course Information
Course Code COMP SCI 3314 Course Introduction to Statistical Machine Learning Coordinating Unit School of Computer Science Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 2 hours per week. Available for Study Abroad and Exchange Y Prerequisites COMP SCI 2201 Incompatible COMP SCI 4401, COMP SCI 4801 Assumed Knowledge Basic probability theory and linear algebra as taught in MATHS 1004 or MATHS 1012 Course Description Statistical Machine Learning is concerned with algorithms that automatically improve their performance through 'learning'. For example, computer programs that learn to detect humans in images/video; predict stock markets, and rank web pages. Statistical machine learning has emerged mainly from computer science and artificial intelligence, and has connections to a variety of related subjects including statistics, applied mathematics and pattern analysis. Applications include image and audio signal analysis, data mining, bioinformatics and exploratory data analysis in natural science and engineering. This is an introductory course on statistical machine learning which presents an overview of many fundamental concepts, popular techniques, and algorithms in statistical machine learning. It covers basic topics such as dimensionality reduction, linear classification and regression as well as more recent topics such as ensemble learning/boosting, support vector machines, kernel methods and manifold learning. This course will provide the students the basic ideas and intuition behind modern statistical machine learning methods. After studying this course, students will understand how, why, and when machine learning works on practical problems.
Course Coordinator: Dr Lingqiao Liu
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesOn successful completion of this course students will be able to:
1 Explain the basic concepts of machine learning, and classic algorithms such as Support Vector Machines and Neural Networks, Deep Learning.
2 Explain the basic principles and theory of machine learning, which may guide students to invent their own algorithms in future.
3 Ability to program the algorithms in the course.
4 Ability to do mathematical derivation of the algorithms in the course.
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) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
1,2,3,4 Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
1,2,3,4 Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
3,4 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
1,2 Intercultural and ethical competency
- adept at operating in other cultures
- comfortable with different nationalities and social contexts
- able to determine and contribute to desirable social outcomes
- demonstrated by study abroad or with an understanding of indigenous knowledges
1,2 Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
- No textbook required.
- 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 Python or Matlab, C/C++ is required.
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 LearningOur 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 ModesThe course will be primarily delivered through two activities:
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.
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 SummaryStudents 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 RequirementsKnowing 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.
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 SummaryThe course includes the following assessment components:
- Final written exam at 50%.
- Four assignments at .
DetailsComponent Weighting Learning Outcomes CBOK Areas
Assignments 5% 15% 15% 15%, 4 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
- Interpersonal Communication
- Societal Issues
- History & Status of the Discipline
- Hardware & Software
- Data & Information
- Human Computer Interfaces
- Systems Development
Details of the Australian Computer Society's Core Bode of Knowledge (CBOK) can be found in this document.
Assessment Related RequirementsHurdle 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 DetailFinal 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.
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.
Assignment solutions are to be submitted through Canvas.
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.
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.
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 Access Adelaide.
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.
- Academic Support with Maths
- Academic Support with writing and speaking skills
- Student Life Counselling Support - Personal counselling for issues affecting study
- International Student Support
- AUU Student Care - Advocacy, confidential counselling, welfare support and advice
- Students with a Disability - Alternative academic arrangements
- Reasonable Adjustments to Teaching & Assessment for Students with a Disability Policy
- LinkedIn Learning
Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangement Policy
- Academic Honesty Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Elder Conservatorium of Music Noise Management Plan
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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’s disciplinary procedures.
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