Statistical Machine Learning (COMP90051)
Graduate courseworkPoints: 12.5On Campus (Parkville)
About this subject
Contact information
Semester 2
Ben Rubinstein
Overview
| Availability | Semester 2 |
|---|---|
| Fees | Look up fees |
AIMS
With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events.
Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks.
This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems.
INDICATIVE CONTENT
Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods.
Intended learning outcomes
INTENDED LEARNING OUTCOMES (ILO)
On completion of this subject the student is expected to:
- Describe a range of machine learning algorithms
- Design, implement and evaluate learning systems to solve real-world problems, based on an appreciation of their relative suitability to different tasks
Generic skills
On completion of the subject students should have the following skills:
- Ability to undertake problem identification, formulation, and solution
- Ability to utilise a systems approach to complex problems and to design and operational performance ability to manage information and documentation
- Capacity for creativity and innovation
- Ability to communicate effectively both with the engineering team and the community at large.
Last updated: 3 November 2022
Eligibility and requirements
Prerequisites
One of the following:
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| COMP30018 | Knowledge Technologies | No longer available | |
| COMP90049 | Knowledge Technologies |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
| COMP30027 | Machine Learning | Semester 1 (On Campus - Parkville) |
12.5 |
OR Admission in to one of the following courses:
- MC-IT Master of Information Technology, 100 or 150 point program in Distributed Computing or Computing
- MC-IT Master of Information Technology, 100 point program in Cyber Security
Corequisites
None
Non-allowed subjects
433-484 Machine Learning
433-679 Evolutionary and Neural Computation
433-680 Machine Learning
433-684 Machine Learning
Recommended background knowledge
Basic probability
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 3 November 2022
Assessment
Additional details
- Two projects due around weeks 7 and 11, requiring approximately 65 - 70 hours of work in total (50%)
- An end-of-semester examination not exceeding 3 hours (50%).
Hurdle requirement: To pass the subject, students must obtain:
- A mark of at least 25/50 on the exam
- and also a combined mark of at least 25/50 for the projects.
Assessment for this subject addresses both Intended Learning Outcomes (ILOs)
Last updated: 3 November 2022
Dates & times
- Semester 2
Principal coordinator Ben Rubinstein Mode of delivery On Campus (Parkville) Contact hours 36 hours, comprising of two 1-hour lectures and one 1-hour workshop per week Total time commitment 200 hours Teaching period 29 July 2019 to 27 October 2019 Last self-enrol date 9 August 2019 Census date 31 August 2019 Last date to withdraw without fail 27 September 2019 Assessment period ends 22 November 2019 Semester 2 contact information
Ben Rubinstein
Time commitment details
200 hours
Last updated: 3 November 2022
Further information
- Texts
- Subject notes
LEARNING AND TEACHING METHODS
The subject is delivered through a combination of lectures and tutorials. One feature of the subject is that the projects are designed to be relatively open-ended and broad enough that students have scope to get hands-on experience implementing the breadth of material covered in the subject, as well as building off the subject content in innovating their own methods/researching related methods from the research literature and implementing them themselves.
INDICATIVE KEY LEARNING RESOURCES
Students will have access to lecture slides, readings relating to the lecture materials (both from a textbook and conference/journal papers), tutorial worksheets with worked solutions for all numeric problems, and sample reports to use in writing the project reports. Students are permitted to do their programming in any language and any programming environment/OS.
CAREERS / INDUSTRY LINKS
Machine learning has been growing rapidly in industry over the past two decades, with key industry players including Google, Microsoft, Amazon, Facebook and Twitter. There have been guest lecturers in the subject from organisations such as NICTA, which has a strong interest in machine learning (indeed one of the primary research groupings within NICTA is based on Machine Learning).
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Commerce (Decision, Risk and Financial Sciences) Course Master of Data Science Course Ph.D.- Engineering Course Master of Philosophy - Engineering Course Doctor of Philosophy - Engineering Course Master of Science (Computer Science) Specialisation (formal) Distributed Computing Specialisation (formal) Computing - Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Please note Single Subject Studies via Community Access Program is not available to student visa holders or applicants
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
Additional information for this subject
Subject coordinator approval required
- Available to Study Abroad and/or Study Exchange Students
Last updated: 3 November 2022