COMP SCI 3301 - Advanced Algorithms

North Terrace Campus - Semester 1 - 2015

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.

  • General Course Information
    Course Details
    Course Code COMP SCI 3301
    Course Advanced Algorithms
    Coordinating Unit Computer Science
    Term Semester 1
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 3 hours per week
    Available for Study Abroad and Exchange Y
    Prerequisites COMP SCI 2201
    Assessment Written exam, Assignments
    Course Staff

    Course Coordinator: Dr Mingyu Guo

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    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)
    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
  • Learning Resources
    Required Resources

    Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein, Introduction to Algorithms, Third Edition, MIT Press
    Recommended 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, 1-378
    Online Learning
  • 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 algorithms

    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.
    Small Group Discovery Experience
    Small group discovery experience is devloped through working on the assignments collaboratively with the team (2 students).
  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    3 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 Requirements
    Students 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 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 instructions will be provided during the course.
    Course Grading

    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.

  • 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 ( 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.

  • Student Support
  • Policies & Guidelines
  • 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’s disciplinary procedures.

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