Note: This is an archived Handbook entry from 2016.
|Dates & Locations:|| |
This subject has the following teaching availabilities in 2016:Semester 1, Parkville - Taught on campus.
Semester 2, Parkville - Taught on campus.
Timetable can be viewed here. For information about these dates, click here.
|Time Commitment:||Contact Hours: 36 hours, comprising of two 1-hour lectures and one 1-hour workshop per week |
Total Time Commitment:
One of the following:
Study Period Commencement:
Semester 1, Semester 2
|Recommended Background Knowledge:|| |
|Non Allowed Subjects:|| |
Students cannot enrol in and gain credit for this subject and:
433-352 Data on the Web
|Core Participation Requirements:||
For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.
It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability
CoordinatorAssoc Prof Karin Verspoor, Prof Rao Kotagiri
Semester 1, Prof Rao Kotagiri
Semester 2, A/Prof Karin Verspoor
Much of the world's knowledge is stored in the form of unstructured data (e.g. text) or implicitly in structured data (e.g. relational databases). In this subject students will learn algorithms and data structures for extracting, retrieving and storing analysing explicit knowledge from various data sources, with a focus on the web.
The aim of this subject is to introduce students to knowledge technologies and to provide a foundational knowledge of data science. The subject will also give students exposure to what applied research is all about.
Topics include: data encoding and markup, web crawling, regular expressions, document indexing, text retrieval, basic probability, clustering, pattern mining, Bayesian learning, instance-based learning, and prediction and approaches to evaluation of knowledge technologies.
Examples of projects that students may complete are:
INTENDED LEARNING OUTCOMES (ILO)
Having completed this unit the student is expected to describe and apply the fundamentals of knowledge systems, including data acquisition and aggregation, knowledge extraction, text retrieval, machine learning and data mining.
On completion of this subject the student is expected to:
Hurdle requirement: To pass the subject, students must obtain at least:
ILO 1 is addressed in the projects (applied) and the mid-semester test and final exam (theoretical). ILO 2 is addressed in the projects (through using a range of systems that are provided to students or that students experiment with themselves). ILO 3 is also addressed in the projects (which are generally themed around projects underway at the University, to give them a more applied feel).
|Prescribed Texts:|| |
|Breadth Options:|| |
This subject potentially can be taken as a breadth subject component for the following courses:
You should visit learn more about breadth subjects and read the breadth requirements for your degree, and should discuss your choice with your student adviser, before deciding on your subjects.
|Fees Information:||Subject EFTSL, Level, Discipline & Census Date|
On completion of this subject, students should have developed the following skills:
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 such methods 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, and may be given the option of working in a team (with suitably increased expectations on what they are required to do). In recent years, the projects have been hosted on Kaggle, supporting a live “scoreboard” for student systems, and giving the projects more of a real-world feel.
CAREERS / INDUSTRY LINKS
The knowledge technologies industry (encompassing machine learning, data science, natural language processing and information retrieval) has been growing rapidly over the past two decades, with key industry players including Google, Microsoft, Amazon, Facebook and Twitter. Google has sponsored a prize for the highest-achieving student in the subject each in recent years, underlining its interest in the subject material. There have been guest lecturers in the subject from organisations including Palantir Technologies and NICTA.
B-ENG Software Engineering stream |
Master of Engineering (Software with Business)
Master of Engineering (Software)
Science-credited subjects - new generation B-SCI and B-ENG.
Selective subjects for B-BMED
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