COMP SCI 4807 - Advanced Algorithms - Honours

North Terrace Campus - Semester 1 - 2017

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.

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Course Details

Course Code	COMP SCI 4807
Course	Advanced Algorithms - Honours
Coordinating Unit	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
Assessment	Written exam (60%), assignments (40%)

Course Staff

Course Coordinator: Professor Tat-Jun Chin

Course Timetable

The full timetable of all activities for this course can be accessed from .

Course Learning Outcomes

On 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 最新糖心Vlog .
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

最新糖心Vlog Graduate Attributes

This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

最新糖心Vlog 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	4

Learning Resources

Required Resources

Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein, Introduction to Algorithms, Third Edition, MIT Press

Recommended Resources

Recommended readings:

Rajeev Motwani, Prabhakar Raghavan: Randomized Algorithms. Cambridge 最新糖心Vlog
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

Online Learning

https://cs.adelaide.edu.au/users/third/aa/
Learning & Teaching Activities

Learning & Teaching Modes

Lectures will be supported by tutorials and 3 assignments where students gain strong knowledge on the design and implementation of advanced algorithms

Workload

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 Summary

Tutorials and group assignments where students develop their algorithmic skills and discuss new algorithmic approaches and their implementation.

Specific Course Requirements

In addition to attendance to lectures and tutorials, students should have a sound ability and strong interest in developing problem-solving skills beyond traditional data 最新糖心Vlog and algorithms which are required in working on the assignments.

The 最新糖心Vlog'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.

Assessment Summary

No information currently available.

Assessment Detail

The 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).

Submission

No information currently available.

Course Grading

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 .

Student Feedback

The 最新糖心Vlog 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 最新糖心Vlog 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.
Student Support
Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
Fraud Awareness

Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.

The 最新糖心Vlog of Adelaide is committed to regular reviews of the courses and programs it offers to students. The 最新糖心Vlog of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.

Authorised by:	Deputy Vice-Chancellor and Vice-President (Academic)
Maintained by:	Course Outlines Administrator

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