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 one 2-hour lecture and one 1-hour tutorial. |
Total Time Commitment:
An undergraduate degree in a cognate discipline.
|Recommended Background Knowledge:|| |
Basic discrete mathematics (sets and relations); elementary data structures (arrays, records, and linked lists).
|Non Allowed Subjects:|| |
|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 Harald Sondergaard, Prof Lars Kulik
Prof Lars Kulik
A/Prof Harald Sondergaard
The aim of this subject is for students to develop familiarity and competence in assessing and designing computer programs for computational efficiency. Although computers manipulate data very quickly, to solve large-scale problems, we must design strategies so that the calculations combine effectively. Over the latter half of the 20th century, an elegant theory of computational efficiency developed. This subject introduces students to the fundamentals of this theory and to many of the classical algorithms and data structures that solve key computational questions. These questions include distance computations in networks, searching items in large collections, and sorting them in order.
Topics covered include complexity classes and asymptotic notation; empirical analysis of algorithms; abstract data types including queues, trees, priority queues and graphs; algorithmic techniques including brute force, divide-and-conquer, dynamic programming and greedy approaches; space and time trade-offs; and the theoretical limits of algorithm power.
INTENDED LEARNING OUTCOMES (ILO)
On completion of this subject the student should be able to:
The examination is a hurdle and must be passed to pass the subject
The student must also successfully complete at least eight of the weekly online quizzes.
|Prescribed Texts:|| |
A. Levitin, Introduction to the Design and Analysis of Algorithms, Pearson, 3rd edition, 2012
|Breadth Options:|| |
This subject is not available as a breadth subject.
|Fees Information:||Subject EFTSL, Level, Discipline & Census Date|
On completion of this subject students should have the following skills:
LEARNING AND TEACHING METHODS
The subject involves two weekly one -hour lectures and one tutorial class. The lectures are a mix of direct delivery and interactive student problem solving. Although written assignments are submitted by students individually, in-plenum discussion of the problems is encouraged.
INDICATIVE KEY LEARNING RESOURCES
Students are provided with lecture slides, and links on the LMS to the in-house animated software Algorithms in Action. The slides are integrated with a well-established textbook.
CAREERS / INDUSTRY LINKS
With Big Data at the forefront of modern computing solutions, industry is ever-more focused on efficient computational analysis methods. Software engineers, developers and data analysts will find not only the analysis techniques, but also the fundamental algorithmic design concepts, highly applicable to the handling of significant datasets. Building on an initial connection in a similar undergraduate offering, there is scope for industry liaison with this subject.
Doctor of Philosophy - Engineering |
Master of Information Technology
Master of Operations Research and Management Science
Master of Philosophy - Engineering
Master of Science (Bioinformatics)
Approved Masters level subjects from other departments |
MIT Computing Specialisation
MIT Distributed Computing Specialisation
MIT Health Specialisation
MIT Spatial Specialisation
Master of Engineering (Software with Business)
Master of Engineering (Software)
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