COMP SCI 7314 - Introduction to Statistical Machine Learning
North Terrace Campus - Trimester 2 - 2022
General Course Information
Course Code COMP SCI 7314 Course Introduction to Statistical Machine Learning Coordinating Unit School of 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 Y Prerequisites COMP SCI 7103, COMP SCI 7202, COMP SCI 7202B, COMP SCI 7208 or COMP SCI 7211 Restrictions Not available for Master of Computer Science/Software Engineering students 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 Alfred Krzywicki
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 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.
Attribute 7: Digital capabilities
Graduates are well prepared for living, learning and working in a digital society.
Required Resources1. 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.
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 ModesThis 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.
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 SummaryYou 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 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 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 DetailFull descriptions of the assessment tasks and associated grading rubrics are in the Assignments space on the MyUni course site. You will have opportunities to get further clarification on assessment tasks as needed.
SubmissionUnless 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 University of Adelaide Academic Integrity policies.
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.
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This section contains links to relevant assessment-related policies and guidelines - all university policies.
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- Modified Arrangements for Coursework Assessment
- Student Experience of Learning and Teaching Policy
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