成人大片

COMP SCI 7201MELB - Algorithm & Data Structure Analysis

Melbourne Campus - Semester 1 - 2024

This course provides an introduction to program development techniques with a focus on basic ideas of correctness and proof. The course introduces, among others, notions of complexity and analysis, recursion, abstract data types, representation of lists, stacks, queues, sets, trees and hash tables, graphs and Graph Traversal. The course allows students to experience different approaches to problem solving.

  • General Course Information
    Course Details
    Course Code COMP SCI 7201MELB
    Course Algorithm & Data Structure Analysis
    Coordinating Unit Computer Science
    Term Semester 1
    Level Postgraduate Coursework
    Location/s Melbourne Campus
    Units 3
    Contact Up to 3 hours per week
    Available for Study Abroad and Exchange
    Prerequisites COMP SCI 7103, COMP SCI 7202, (COMP SCI 7210 and COMP SCI 7211), COMP SCI 7202B or COMP SCI 7208
    Incompatible COMP SCI 7082
    Restrictions Available only to 成人大片 College Melbourne Campus students
    Assessment Written exam and/or assignments
    Course Staff

    Course Coordinator: Dr Menasha Thilakaratne

    Lecturer: Dr Lye Kong Wei
    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    On successful completion of this course students will be able to:

     
    1 Skills in performing analysis of given recursive and iterative algorithms.
    2 Understanding and performing simple proofs of algorithmic complexity and correctness.
    3 An ability to understand and derive recurrences describing algorithms and properties of data structures. 
    4 An understanding of the implementation and efficiency of a range of data structures including, trees, binary heaps, hash-tables and graphs. 
    5 An understanding of a variety of well-known algorithms on some of the data structures presented. 
    6 The ability to implement and use these algorithms in code.
    7 A foundational understanding of intractability. An understanding of proof techniques for NP-Completeness. 
    8 An ability to solve new analytic and algorithmic 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.

    1-8

    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.

    1,2,8

    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.

    1-8

    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.

    1,6,8

    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.

    8

    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.

    1,2,5,6,8
  • Learning Resources
    Required Resources
    Textbook
    The textbook for this course is Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein, Introduction to Algorithms, Third Edition, MIT Press.
    Recommended Resources
    Recommended further reading:
    Algorithms and Data Structures - The Basic Toolbox by Kurt Mehlhorn and Peter Sanders, Springer, 2008.(the full text is available on the Author’s website).
    Online Learning
    Course Website:
  • Learning & Teaching Activities
    Learning & Teaching Modes

    No information currently available.

    Workload

    No information currently available.

    Learning Activities Summary

    No information currently available.

  • 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

    No information currently available.

    Assessment Detail

    No information currently available.

    Submission

    No information currently available.

    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 .

  • 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 (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.

  • 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鈥檚 disciplinary procedures.

The 成人大片 is committed to regular reviews of the courses and programs it offers to students. The 成人大片 therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.