Note: This unit is an archived version! See Overview tab for delivered versions.
COMP5211: Algorithms (2014 - Semester 2)
| Unit: | COMP5211: Algorithms (6 CP) |
| Mode: | Normal-Day |
| On Offer: | Yes |
| Level: | Postgraduate |
| Faculty/School: | School of Computer Science |
| Unit Coordinator/s: |
Dr Viglas, Anastasios
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| Session options: | Semester 2 |
| Versions for this Unit: |
| Campus: | Camperdown/Darlington |
| Pre-Requisites: | None. |
| Brief Handbook Description: | The study of algorithms is a fundamental aspect of computing. This unit of study covers data structures, algorithms, and gives an overview of the main ways of computational thinking from simple list manipulation and data format conversion, up to shortest paths and cycle detection in graphs. Students will gain essential knowledge in computer science, including basic concepts in data structures, algorithms, and intractability, using paradigms such as dynamic programming, divide and conquer, greed, local search, and randomisation, as well NP-hardness. |
| Assumed Knowledge: | This unit of study assumes that students have general knowledge of mathematics (especially Discrete Math) and problem solving. Having moderate knowledge about Data structure can also help students to better understand the concepts of Algorithms will be taught in this course. |
| Lecturer/s: |
Dr Viglas, Anastasios
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| Timetable: | COMP5211 Timetable | ||||||||||||||||||||
| Time Commitment: |
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| T&L Activities: | Lecture: Lecture Tutorial: Tutorial |
Attributes listed here represent the key course goals (see Course Map tab) designated for this unit. The list below describes how these attributes are developed through practice in the unit. See Learning Outcomes and Assessment tabs for details of how these attributes are assessed.
| Attribute Development Method | Attribute Developed |
| Understand main concepts of Graphs. | Design (Level 2) |
For explanation of attributes and levels see Engineering & IT Graduate Outcomes Table.
Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.
Design (Level 2)| Assessment Methods: |
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| Assessment Description: |
Assignment: Short weekly assignments, that may include implementations of algorithms. Quiz: Short weekly quizzes, during tutorial times, on e-learning. Final Exam: Final Examination |
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| Grading: |
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| Policies & Procedures: | IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism. In assessing a piece of submitted work, the School of IT may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so. Other policies See the policies page of the faculty website at http://sydney.edu.au/engineering/student-policies/ for information regarding university policies and local provisions and procedures within the Faculty of Engineering and Information Technologies. |
| Prescribed Text/s: |
Note: Students are expected to have a personal copy of all books listed.
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Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar https://web.timetable.usyd.edu.au/calendar.jsp
| Week | Description |
| Week 1 | Unit introduction, algorithms and complexity, motivation and course outline. |
| Week 2 | Introduction to graph algorithms: BFS and DFS |
| Week 3 | Greedy algorithms: Interval scheduling, Kruskal's algorithm, Dijkstra's algorithm |
| Week 4 | Divide and conquer: Recurrences, sorting, integer multiplication, selection |
| Week 5 | Dynamic programming: weighted interval scheduling, longest increasing subsequence, knapsack |
| Week 6 | Dynamic programming: Nim, RNA structure |
| Week 7 | Network flows, maxflow mincut theorems, matching |
| Week 8 | Max-flow and min-cut applications: Disjoint paths, airline scheduling, project section |
| Week 9 | Introduction to complexity theory: Polynomial time reductions, P, NP, and NP-completeness |
| Week 10 | Coping with hardness: Solving special cases, approximations, local search |
| Week 11 | Beyond NP: Weakly NP-hard, coNP, PSPACE |
| Week 12 | Advanced topic such as fixed parameter tractability, hashing, other complexity classes, etc. |
| Week 13 | Review and outlook |
| Exam Period | Assessment Due: Final Exam |
Course Relations
The following is a list of courses which have added this Unit to their structure.
Course Goals
This unit contributes to the achievement of the following course goals:
| Attribute | Practiced | Assessed |
| Design (Level 2) | Yes | 100% |
| Maths/Science Methods and Tools (Level 2) | No | 0% |
| Engineering/IT Specialisation (Level 2) | No | 0% |
These goals are selected from Engineering & IT Graduate Outcomes Table which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.
