COMP SCI 4807 - Advanced Algorithms
North Terrace Campus - Semester 1 - 2020
General Course Information
Course Code COMP SCI 4807 Course Advanced Algorithms 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 N Prerequisites COMP SCI 2201 Incompatible COMP SCI 3301, COMP SCI 4407 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: Dr Mingyu Guo
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 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 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
University Graduate Attribute Course Learning Outcome(s)
- 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)
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
- 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
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
- 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
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
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
WorkloadThe 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.
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment must maintain academic standards.
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 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.
Final results for this course will be made available through Access Adelaide.
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