COMP SCI 3301 - Advanced Algorithms
North Terrace Campus - Semester 1 - 2014
General Course Information
Course Code COMP SCI 3301 Course Advanced Algorithms Coordinating Unit School of Computer Science Term Semester 1 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Prerequisites COMP SCI 2201 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: Adjunct Professor Hong Shen
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning Outcomes
- Students should develop a sound theoretical understanding of advanced algorithms and practical problem solving skills using them.
- Students should develop basic knowledge of a wide range of advanced algorithm design techniques including dynamic programming, linear programming, approximation algorithms, and randomized algorithms.
- Students should develop basic advanced algorithm analysis skills for analyzing the approximation ratio of approximation algorithms and the probability of randomized algorithms.
- Students should gain a good understanding on a wide range of advanced algorithmic problems, their relations and variants, and application to real-world problems.
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) Knowledge and understanding of the content and techniques of a chosen discipline at advanced levels that are internationally recognised. 1,2,3,4 The ability to locate, analyse, evaluate and synthesise information from a wide variety of sources in a planned and timely manner. 2,3 An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems. 2,3 Skills of a high order in interpersonal understanding, teamwork and communication. 1,2,3,4 A proficiency in the appropriate use of contemporary technologies. 2,3 A commitment to continuous learning and the capacity to maintain intellectual curiosity throughout life. 1,2,3,4 A commitment to the highest standards of professional endeavour and the ability to take a leadership role in the community. 1,4 An awareness of ethical, social and cultural issues within a global context and their importance in the exercise of professional skills and responsibilities. 1,4
Thomas 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 tutorial and 3 assignments where students gain strong knowledge on the design and implementation of advanced algorithms
No information currently available.
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 attnedence to lectrues 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.
Small Group Discovery ExperienceSmall group discovery experience is devloped through working on the assignments collaboratively with the team (2 students).
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 Summary3 assignments worth 30% (each worth 10% of the final mark)
Final Exam worth 70%
For P/G stduents at least one assignment will contain a component that would require a deeper understanding to the learnt knowledge than U/G students.
Assessment Related RequirementsStudents have to achieve at least 40% of the assignment marks, 40% of the exam marks, and overall at least 50% in order to pass the course.
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.
SubmissionSubmission instructions will be provided during the course.
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending
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.
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