COMP SCI 7407 - Advanced Algorithms
North Terrace Campus - Semester 1 - 2022
General Course Information
Course Code COMP SCI 7407 Course Advanced Algorithms Coordinating Unit School of Computer Science Term Semester 1 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact 2 hours per week Available for Study Abroad and Exchange Y Prerequisites COMP SCI 7201 Incompatible COMP SCI 7301 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 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.
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)
Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
1 - 4
Attribute 2: Creative and critical thinking, and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.
1 - 4
Attribute 3: Teamwork and communication skills
Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.
Attribute 4: Professionalism and leadership readiness
Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.
1 - 4
Attribute 5: Intercultural and ethical competency
Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.
Attribute 7: Digital capabilities
Graduates are well prepared for living, learning and working in a digital society.
Attribute 8: Self-awareness and emotional intelligence
Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.
Required ResourcesAll required resources for this course will be provided online via the MyUni platform.
Recommended ResourcesThere are no recommended resources for this course.
Learning & Teaching Activities
Learning & Teaching ModesLectures will be supported by workshops 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.Students are expected to spend 9-10 hours per week on this course.
There will be 3-4 hours contact time for learning and teaching activities and students will be working in groups and individually 6-7 hours to carry out the required learning and teaching activities for acquiring the expected knowledge, understanding, and skills in this course.
Learning Activities SummaryWeek 1-2: Course Overview and Dynamic Program;
Week 3-4: Linear Program;
Week 5-6: Approximation Algorithms;
Week 7: Fixed Parameter Algorithm;
Week 8-9: Randomized Algorithms;
Week 10: Computational Geometry;
Week 11-12: Recent Trends in Algorithmic Research
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.
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
Assessment DetailEach 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 coding assignments will be given around week 2, 5 and 8 respectively. The final assessment will be open book in the form of a week-long take-home assignment.
SubmissionSubmission details for all activities are available in MyUni but the majority of your submissions will be online and may be subjected to originality testing through Turnitin or other mechanisms. You will receive clear and timely notice of all submission details in advance of the submission date.
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.
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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