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FIT1029 Algorithmic problem solving - Semester 2, 2013

Algorithms are recipes for solving a problem. They are fundamental to computer science and software engineering. Algorithms are the formal foundation of computer programming but also exist independently of computers as systematic problem-solving procedures. This unit introduces algorithmics, the study of algorithms. It is not about programming and coding but rather about understanding and analysing algorithms and about algorithmic problem-solving, i.e. the design of systematic problem-solving procedures. The unit will not require any knowledge of a programming language and is very hands-on. Students will develop algorithms to solve a wide variety of different problems, working individually as well as together in groups and as a class.

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; understanding the efficiency of an algorithm; and limitations of algorithms.

Mode of Delivery

Clayton (Day)

Contact Hours

2 hrs lectures/wk, 2 hrs tutorials/wk

Workload requirements

Students will be expected to spend 12 hours per week on various activities including:

  • 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.

Chief Examiner

Campus Lecturer


Dr. David Albrecht

Academic Overview

Learning Outcomes

At the completion of this unit students will be able to -
  • describe an algorithm consisting of basic structures (sequence, choice, iteration, modules) at the level of detail required for a particular audience;
  • demonstrate how basic data structures (list, graphs, trees, sets, tables) function;
  • create simple recursive and iterative algorithms;
  • evaluate different possible strategies for developing an algorithm and be able to select an appropriate one to solve a given problem;
  • apply standard patterns to develop algorithms;
  • break problems down into simpler problems;
  • determine the complexity of simple algorithms;
  • recognize the limitations of algorithms.

Unit Schedule

Week Activities Assessment
0   No formal assessment or activities are undertaken in week 0
1 Introduction to the unit, algorithms and data structures. Peer Instruction Participation recorded in every lecture
2 Describing and finding algorithms.  
3 Invariance and greedy approach.  
4 Applying Brute Force to solve problems. Assignment 1 due
5 Decomposition.  
6 Decrease and conquer. Test during lecture
7 Transform and conquer; and divide and conquer.  
8 Recursion and backtracking.  
9 Dynamic Programming.  
10 Complexity. Assignment 2 due
11 Limitations of algorithms.  
12 Strategies for finding algorithms.  
  SWOT VAC No formal assessment is undertaken in SWOT VAC
  Examination period LINK to Assessment Policy: http://policy.monash.edu.au/policy-bank/

*Unit Schedule details will be maintained and communicated to you via your learning system.

Assessment Summary

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

Assessment Task Value Due Date
Assignment 1 10% Week 4
Test 15% Week 6
Assignment 2 10% Week 10
Peer Instruction Participation in Lecture Sessions 5% Every lecture
Examination 1 60% To be advised

Teaching Approach

  • Lecture and tutorials or problem classes
    This teaching and learning approach helps students to initially encounter information at lectures, and discuss and explore the information during tutorials. 
  • Problem-based learning
    You will be presented with information and guided on how to best find solutions for a given problem. 

Assessment Requirements

Assessment Policy

Faculty Policy - Unit Assessment Hurdles (http://www.infotech.monash.edu.au/resources/staff/edgov/policies/assessment-examinations/unit-assessment-hurdles.html)

Academic Integrity - Please see the Demystifying Citing and Referencing tutorial at http://lib.monash.edu/tutorials/citing/

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:
    Week 4
  • Assessment task 2
    This test will be based on the first 4 weeks of material.
    Criteria for assessment:

    This test will evaluate:

    1. Your understanding of the material provided during the first few weeks of semester.
    2. Your capability to write simple algorithms in pseudo code given a clear specification.
    3. Your ability to demonstrate an understanding of some basic data structures.
    4. Your ablility to analyse the behaviour of simple algorithms.
    Due date:
    Week 6
  • Assessment task 3
    Assignment 2
    This assignment will help you understand different strategies for finding algorithms.
    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:
    Week 10
  • Assessment task 4
    Peer Instruction Participation in Lecture Sessions
    Student participation through the response gathering system will be recorded.
    Criteria for assessment:

    Student answers during the peer instruction session will not be graded based on correctly answering questions. The grade will be based on participation. A full mark will be awarded if student answers at least 80% of the questions throughout the semester. 0 mark will be awarded if student answers less than 80% of total questions presented in the semester during lecture.

    Due date:
    Every lecture


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

Learning resources

Reading list

Levitin, A., (2007) Introduction to the Design and Analysis of Algorithms, (2nd Edition), Addison-Wesley.

Monash Library Unit Reading List

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
  • Test results and feedback

Extensions and penalties

Returning assignments

Assignment submission

It is a University requirement (http://www.policy.monash.edu/policy-bank/academic/education/conduct/plagiarism-procedures.html) for students to submit an assignment coversheet for each assessment item. Faculty Assignment coversheets can be found at http://www.infotech.monash.edu.au/resources/student/forms/. Please check with your Lecturer on the submission method for your assignment coversheet (e.g. attach a file to the online assignment submission, hand-in a hard copy, or use an online quiz). Please note that it is your responsibility to retain copies of your assessments.

Online submission

If Electronic Submission has been approved for your unit, please submit your work via the learning system for this unit, which you can access via links in the my.monash portal.

Recommended text(s)

Levitin, A. (2007). Introduction to the Design and Analysis of Algorithms. (2nd) Addison-Wesley.

Other Information


Graduate Attributes Policy

Student services

Monash University Library

Disability Liaison Unit

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.

Your feedback to Us

Previous Student Evaluations of this Unit

This unit was first offered in 2010 and in Semester 1, 2012, some of those students were taking third year units.  Evidence gathered over the years indicates that the introduction of FIT1029 has helped to improve the students' programming skills and improved the pass rates in core BCS and BSE units.

Feedback from students who have taken FIT1029 and have gone on to later year studies in the BCS and the BSE indicates that students feel that FIT1029 has helped them gain a better appreciation of algorithms, and to regard thinking about algorithms as an activity that should be kept separated from coding algorithms into a particular computer programming language.

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

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