COMP SCI 4807 - Advanced Algorithms - Honours
North Terrace Campus - Semester 1 - 2017
General Course Information
Course Code COMP SCI 4807 Course Advanced Algorithms - Honours Coordinating Unit School of Computer Science Term Semester 1 Level Undergraduate Location/s North Terrace Campus Units 3 Contact 2 hours per week Available for Study Abroad and Exchange Prerequisites COMP SCI 2201 Incompatible COMP SCI 3301 Course Description The development of a sound theoretical understanding of advanced algorithms and practical problem solving skills using them. Advanced algorithm topics chosen from: Dynamic Programming, Linear Programming, Matching, Max Flow / Min Cut, P and NP, Approximation Algorithms, Randomized Algorithms, Computational Geometry.
Course Coordinator: Professor Tat-Jun Chin
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesOn successful completion of this course students will be able to:
1 Students should develop a sound theoretical understanding of advanced algorithms and practical problem solving skills using them. 2 Students should develop basic knowledge of a wide range of advanced algorithm design techniques including dynamic programming, linear programming, approximation algorithms, and randomized algorithms. 3 Students should develop basic advanced algorithm analysis skills for analyzing the approximation ratio of approximation algorithms and the probability of randomized algorithms. 4 Students should gain a good understanding on a wide range of advanced algorithmic problems, their relations and variants, and application to real-world problems.
The above course learning outcomes are aligned with the Engineers Australia Stage 1 Competency Standard for the Professional Engineer.
The course is designed to develop the following Elements of Competency: 1.1 1.2 1.3 1.4 1.5 1.6 2.1 2.2 2.3 2.4 3.1 3.2 3.3 3.4 3.5 3.6
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
1-4 Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
1-4 Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
4 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
1-4 Intercultural and ethical competency
- adept at operating in other cultures
- comfortable with different nationalities and social contexts
- able to determine and contribute to desirable social outcomes
- demonstrated by study abroad or with an understanding of indigenous knowledges
4 Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
Required ResourcesThomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein, Introduction to Algorithms, Third Edition, MIT Press
Recommended ResourcesRecommended readings:
Rajeev Motwani, Prabhakar Raghavan: Randomized Algorithms. Cambridge University
Press 1995, isbn 0-521-47465-5
Vijay V. Vazirani: Approximation algorithms. Springer 2001, isbn
978-3-540-65367-7, pp. I-IXI, 1-378
Learning & Teaching Activities
Learning & Teaching ModesLectures will be supported by tutorials and 3 assignments where students gain strong knowledge on the design and implementation of advanced algorithms
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Average workload is 12 hours/week (including lecture and tutorial times). A significant amount has to be spend on solving the assignments.
Learning Activities SummaryTutorials and group assignments where students develop their algorithmic skills and discuss new algorithmic approaches and their implementation.
Specific Course RequirementsIn addition to attendance to lectures and tutorials, students should have a sound ability and strong interest in developing problem-solving skills beyond traditional data structures and algorithms which are required in working on the assignments.
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
No information currently available.
Assessment DetailThe written exam will be centrally administered by examinations and held at the end of semester.
Each tutorial will be based on materials presented at that stage of the course or on readings drawn from reference materials. Tutorial questions will be made available on the course webpage.
Three written assignments will be given by week 2, 5 and 8 respectively. Students will be allowed to work on the assignments in teams of up to two people.
Assignment submissions will be marked within one and a half weeks of the submission deadline. Marked sheets with feedback are available for viewing at tutorials.
Below are the CBOK mappings
Abstraction Design Programming
Assignments 5 5 5
Exam 3 3 3
CBOK categories are explained in section 4 of the ICT core body of knowlege. Numbers assigned correspond to the Bloom taxonomy (see page 26 of the same document).
No information currently available.
Grades for your performance in this course will be awarded in accordance with the following scheme:
M11 (Honours Mark Scheme) Grade Grade reflects following criteria for allocation of grade Reported on Official Transcript Fail A mark between 1-49 F Third Class A mark between 50-59 3 Second Class Div B A mark between 60-69 2B Second Class Div A A mark between 70-79 2A First Class A mark between 80-100 1 Result Pending An interim result RP Continuing Continuing CN
Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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