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FIT1002 to demonstrate how pseudo-code algorithms can be mapped to concrete programs.

Topics include: What is a computational problem and what is an algorithm; Basic control structures; basic data structures; Modular Algorithm Structure; Recursion; Problem-solving strategies for algorithm development; Arguing correctness of an algorithm; Arguing termination of an algorithm; Understanding the efficiency of an algorithm; and Limitations of algorithms.

Mode of Delivery

  • Clayton (Day)
  • Sunway (Day)

Contact Hours

2 hrs lectures/wk, 2 hrs tutorials/wk


For on campus students, workload commitments are:

  • two-hour lecture and
  • two-hour tutorial  (requiring advance preparation)
  • a minimum of 2-3 hours of personal study per one hour of contact time in order to satisfy the reading and assignment expectations.
  • You will need to allocate up to 2 hours per week in some weeks, for use of a computer, including time for newsgroups/discussion groups.

Off-campus students generally do not attend lecture and tutorial sessions, however, you should plan to spend equivalent time working through the relevant resources and participating in discussion groups each week.

Unit Relationships


Only for students in the Bachelor of Computer Science and Bachelor of Software Engineering, associated Double Degrees and major/minor sequences. Exceptions can be approved by the unit leader after assessment of mathematical background knowledge.

Chief Examiner

David Albrecht

Campus Lecturer


David Albrecht

Contact hours: Tuesday 1pm - 2pm


Farhad Keissarian

Learning Objectives

At the completion of this unit students will have -
A knowledge and understanding of:

  • the difference between algorithms and processes;
  • basic ways to structure algorithms: basic data structures (simple variables, collections structure, specifically vectors, lists, sets, and tables); basic control structures (sequence, choice, iteration);
  • recursion;
  • modular algorithm structures;
  • the equivalence of recursion and iteration;
  • problem solving strategies suitable for algorithm development including top-down design and bottom-up design;
  • simple standard patterns for algorithms (eg traversal, search);
  • what makes a good algorithm
  • limitations of algorithms (high level).
Developed the skills to:
  • develop simple iterative and recursive algorithms
  • argue the correctness of simple algorithms
  • judge the efficiency of simple algorithms, and
Developed attitudes that enable them to:
  • value clear specification of problems;
  • understand the relation between algorithms and programs;
  • appreciate the value of designing abstract algorithms before starting to code a program;
  • have confidence that they can develop algorithms to solve computational problems;
  • appreciate that seemingly difficult problems can have very simple elegant algorithmic solutions (and vice versa);
  • value correctness arguments for algorithms; and
  • value the importance of simplicity and efficiency.
Demonstrated the communication skills necessary to:
  • solve a problem by discussing possible approaches and solutions as a team; and
  • clearly communicate (the specification of) a computational problem, its algorithmic solution and arguments for correctness and efficiency.

Graduate Attributes

Monash prepares its graduates to be:
  1. responsible and effective global citizens who:
    1. engage in an internationalised world
    2. exhibit cross-cultural competence
    3. demonstrate ethical values
  2. critical and creative scholars who:
    1. produce innovative solutions to problems
    2. apply research skills to a range of challenges
    3. communicate perceptively and effectively

    Assessment Summary

    Examination (3 hours): 60%; In-semester assessment: 40%

    Assessment Task Value Due Date
    Assignment 1 10% March 18th
    Assignment 2 15% May 20th
    Mid Semester Test 15% Week 6
    Examination 1 60% To be advised

    Teaching Approach

    Lecture and tutorials or problem classes
    The approach to teaching and learning include a weekly two-hour lecture and a two-hour tutorial. Additionally, each student should spend a minimum of 8 hours for personal study every week and should allocate up to 5 hours per week in some weeks for use of a computer, including time for newsgroup and discussion.


    Our feedback to You

    Types of feedback you can expect to receive in this unit are:
    • Informal feedback on progress in labs/tutes
    • Graded assignments with comments

    Your feedback to Us

    Monash is committed to excellence in education and regularly seeks feedback from students, employers and staff. One of the key formal ways students have to provide feedback is through SETU, Student Evaluation of Teacher and Unit. The University's student evaluation policy requires that every unit is evaluated each year. Students are strongly encouraged to complete the surveys. The feedback is anonymous and provides the Faculty with evidence of aspects that students are satisfied and areas for improvement.

    For more information on Monash's educational strategy, and on student evaluations, see:

    Previous Student Evaluations of this unit

    If you wish to view how previous students rated this unit, please go to

    Recommended Resources

    Unit Schedule

    Week Date* Activities Assessment
    0 21/02/11   No formal assessment or activities are undertaken in week 0
    1 28/02/11 Introduction to the unit and the type of problems  
    2 07/03/11 Understanding and modelling the problem  
    3 14/03/11 Invariants in problems and data Assignment 1 due 18th March
    4 21/03/11 Decomposition of problems and applying Brute Force to solve problems  
    5 28/03/11 Using abstraction, symmetry, heuristics and divide and conquer to simplify problems  
    6 04/04/11 Recursion Mid Semester Test
    7 11/04/11 Backtracking  
    8 18/04/11 Dynamic Programming  
    Mid semester break
    9 02/05/11 Fundamentals  
    10 09/05/11 Abstract Data Types and Correctness  
    11 16/05/11 Complexity Assignment 2 due 20th May
    12 23/05/11 Limitations of algorithms  
      30/05/11 SWOT VAC No formal assessment is undertaken SWOT VAC

    *Please note that these dates may only apply to Australian campuses of Monash University. Off-shore students need to check the dates with their unit leader.

    Assessment Policy

    To pass a unit which includes an examination as part of the assessment a student must obtain:

    • 40% or more in the unit's examination, and
    • 40% or more in the unit's total non-examination assessment, and
    • an overall unit mark of 50% or more.

    If a student does not achieve 40% or more in the unit examination or the unit non-examination total assessment, and the total mark for the unit is greater than 50% then a mark of no greater than 49-N will be recorded for the unit

    Assessment Tasks


    • Assessment task 1
      Assignment 1
      This assignment will aim to help you understand how to go about finding algorithms to solve problems.
      Criteria for assessment:

      Detailed assessment criteria will be issued along with the assignment.   

      1. All assumptions should be stated.
      2. All algorithms must meet the problem specification.
      3. Students should be able to answer questions about their own work.
      Due date:
      March 18th
    • Assessment task 2
      Assignment 2
      This assignment will aim to help you the importance of fundamental concepts, such as invariants, divide and conquer, induction, dynamic programming and Turing Machines.
      Criteria for assessment:

      Detailed assessment criteria will be issued along with the assignment.   

      1. All assumptions should be stated.
      2. All algorithms must meet the problem specification.
      3. Students should be able to answer questions about their own work.
      Due date:
      May 20th
    • Assessment task 3
      Mid Semester Test
      This test will be based on the first 5 weeks of material.
      Criteria for assessment:
      Due date:
      Week 6


    • Examination 1
      3 hours
      Type (open/closed book):
      Closed book
      Electronic devices allowed in the exam:

    Assignment submission

    Assignment coversheets are available via "Student Forms" on the Faculty website: http://www.infotech.monash.edu.au/resources/student/forms/
    You MUST submit a completed coversheet with all assignments, ensuring that the plagiarism declaration section is signed.

    Extensions and penalties

    Returning assignments


    Monash has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University's academic standards, and to provide advice on how they might uphold them. You can find Monash's Education Policies at:

    Key educational policies include:

    Student services

    The University provides many different kinds of support services for you. Contact your tutor if you need advice and see the range of services available at www.monash.edu.au/students The Monash University Library provides a range of services and resources that enable you to save time and be more effective in your learning and research. Go to http://www.lib.monash.edu.au or the library tab in my.monash portal for more information. Students who have a disability or medical condition are welcome to contact the Disability Liaison Unit to discuss academic support services. Disability Liaison Officers (DLOs) visit all Victorian campuses on a regular basis

    Reading List:

    1. Levitin, A., Introduction to the Design and Analysis of Algorithms (2nd Edition), Addison-Wesley, 2006
    2. Harel, D. with Y. Feldman, Algorithmics: The Spirit of Computing, 3rd ed., Pearson Education Limited, 2004.
    3. Michalewicz, Z. and M. Michalewicz, Puzzle-Based Learning: An introduction to critical thinking, mathematics, and problem solving, Hybrid Publishers, 2008.
    4. Polya, G., How to solve it; a new aspect of mathematical method, 2nd ed., Garden City, N.Y., Doubleday, 1957
    5. Bentley, J., Programming Pearls, Addison-Wesley, 1986
    6. Bentley, J., More Programming Pearls: confessions of a coder, Addison-Wesley,  1988
    7. Skiena, S., The Algorithm Design Manual, TELOS--the Electronic Library of Science, 1998
    8. Cormen, T., C.E. Leiserson, R.L. Rivest, and C. Stein, Introduction to Algorithms, The MIT Press, 1990
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