COMP SCI 4807 - Advanced Algorithms
North Terrace Campus - Semester 1 - 2020
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General Course Information
Course Details
Course Code COMP SCI 4807 Course Advanced Algorithms 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 N Prerequisites COMP SCI 2201 Incompatible COMP SCI 3301, COMP SCI 4407 Assessment Written exam and/or assignments Course Staff
Course Coordinator: Dr Mingyu Guo
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
On successful completion of this course students will be able to:
1 Develop a sound theoretical understanding of advanced algorithms and practical problem solving skills using them. 2 Develop basic knowledge of a wide range of advanced algorithm design techniques including dynamic programming, linear programming, approximation algorithms, and randomized algorithms. 3 Develop basic advanced algorithm analysis skills for analyzing the approximation ratio of approximation algorithms and the probability of randomized algorithms. 4 Explain 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 .
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
1, 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
1, 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
1, 4 -
Learning Resources
Required Resources
Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein, Introduction to Algorithms, Third Edition, MIT PressRecommended Resources
Recommended 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, 1-378Online 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 algorithmsWorkload
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 structures and algorithms which are required in working on the assignments. -
Assessment
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.
Assessment Summary
* The specific due date for each assessment task will be available on MyUni.Assessment Task Weighting (%) Individual/ Group Formative/ Summative Due (week)* Hurdle criteria Learning outcomes CBOK Alignment** Theory assignments 30 Individual Summative Weeks 2-12 1. 2. 3. 4. 1.1 1.2 4.1 Exam 70 Individual Summative NA 1. 2. 3. 4. 1.1 1.2 4.1 Total 100
This assessment breakdown complies with the University's Assessment for Coursework Programs Policy.
**CBOK is the Core Body of Knowledge for ICT Professionals defined by the Australian Computer Society. The alignment in the table above corresponds with the following CBOK Areas:
1. Problem Solving1.1 Abstraction1.2 Design
2. Professional Knowledge2.1 Ethics2.2 Professional expectations2.3 Teamwork concepts & issues2.4 Interpersonal communications2.5 Societal issues2.6 Understanding of ICT profession
3. Technology resources3.1 Hardware & Software3.2 Data & information3.3 Networking
4. Technology Building4.1 Programming4.2 Human factors4.3 Systems development4.4 Systems acquisition
5. ICT Management5.1 IT governance & organisational5.2 IT project management5.3 Service management5.4 Security management
Due to the current COVID-19 situation modified arrangements have been made to assessments to facilitate remote learning and teaching. Assessment details provided here reflect recent updates.
Assignments: No changes. We will still collect via canvas.
Final exam: Per the university policy, we won't have final exams this semester. Advanced Algorithm's final exam will be in the format of open book exam.
3 assignments (10% each) and 1 final exam (70%).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 .
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Student Feedback
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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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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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.
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