Algorithms are the foundation of Computer Science. They enable advances in Artificial Intelligence, Machine Learning, Cybersecurity, Distributed, Mobile, and Cloud Computing, Computer Graphics and Animation, and many more. These advances have made Computer Science an indispensable part of our everyday life.
The main goal of this course is to familiarize students with basic concepts in the design and analysis of algorithms and data structures. It focuses on fundamental computing problems such as sorting and searching. The students will learn how to use different algorithm design methods and data structures to solve such problems efficiently, as well as basic performance measures and analysis techniques.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Display a solid understanding of classical algorithms for fundamental computing problems such as sorting and searching and their analysis.
- Demonstrate deep knowledge of various data structures, such as binary search trees and heaps, and their applications.
- Design efficient algorithms using techniques such as divide-and-concur, greedy approaches, and dynamic programming.
- Analyse the time and space complexity of algorithms.
Indicative Assessment
- Assignments (30) [LO 1,2,4]
- Midterm Test (30) [LO 1,4]
- Final Exam (40) [LO 2,3,4]
The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
Workload
As a Master course, students are expected to recap and conduct independent studies for as many hours as they need to gain understanding of the materials. To help this learning process, we provide 12 two-hour and 12 one-hour lectures, and 11 two-hour tutorial/laboratory sessions. The total worload is expected to be 130 hours.
Inherent Requirements
None
Requisite and Incompatibility
Prescribed Texts
Recommended resources will be mentioned on our webpage and/or Wattle.
T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein. Introduction to Algorithms. MIT Press, 3rd Ed., 2009.
Preliminary Reading
The following reference books are recommended for this course:
- Anany Levitin, Introduction to the Design and Analysis of Algorithms, 3rd ed.
- Baase, S. & Van Gelder, Allen Computer Algorithms — Introduction to Design and Analysis by Addison-Wesley, 3rd Edition, 2000.
- Sedgewick, Robert Algorithms in C, 3rd Edition, 2002.
- Aho, Alfred V., Hopcroft, John E., & Ullman, Jeffrey D. The Design and Analysis of Computer Algorithms, Addison-Wesley, 1974.
- Kleinberg, John & Tardos, Eva Algorithms Design, Addison-Wesley, 2005.
Fees
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2024 | $4980 |
- International fee paying students
| Year | Fee |
|---|---|
| 2024 | $6360 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
Second Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 9231 | 22 Jul 2024 | 29 Jul 2024 | 31 Aug 2024 | 25 Oct 2024 | In Person | N/A |
