|Year of offer||2018|
|Subject level||Graduate coursework|
|Fees||Subject EFTSL, Level, Discipline & Census Date|
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.
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
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.
Eligibility and requirements
One of the following:
|Code||Name||Teaching period||Credit Points|
admission into MCIT-100 program.
433-484 Machine Learning
433-679 Evolutionary and Neural Computation
433-680 Machine Learning
433-684 Machine Learning
Recommended background knowledge
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
- 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)
Dates & times
- Semester 2
Principal coordinator Trevor Cohn 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 23 July 2018 to 21 October 2018 Last self-enrol date 3 August 2018 Census date 31 August 2018 Last date to withdraw without fail 21 September 2018 Assessment period ends 16 November 2018
Semester 2 contact information
Dr Benjamin Rubinstein
Time commitment details
- 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 Doctor of Philosophy - Engineering Course Master of Data Science Course Master of Philosophy - Engineering Course Ph.D.- Engineering Course Master of Commerce (Decision, Risk and Financial Sciences) Course Master of Science (Computer Science) Specialisation (formal) Computing Specialisation (formal) Distributed 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.
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