[an error occurred while processing this directive] [an error occurred while processing this directive]
[an error occurred while processing this directive]
[an error occurred while processing this directive]
Monash University

FIT1029 Algorithmic problem solving - Semester 2, 2015

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)

Workload Requirements

Minimum total expected workload equals 12 hours per week comprising:

(a.) Contact hours for on-campus students:

  • Two hours of lectures
  • One 2-hour tutorial

(b.) Additional requirements (all students):

  • A minimum of 2-3 hours of personal study per one hour of contact time in order to satisfy the reading and assignment expectations.

See also Unit timetable information

Chief Examiner

Campus Lecturer


Dr Kerri Morgan

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 the Student Evaluation of Teaching and Units (SETU) survey. 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, see:

www.monash.edu.au/about/monash-directions/ and on student evaluations, see: www.policy.monash.edu/policy-bank/academic/education/quality/student-evaluation-policy.html

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

Academic Overview

Learning Outcomes

At the successful completion of this unit, students should be able to:
  1. describe an algorithm consisting of basic structures (sequence, choice, iteration, modules) at the level of detail required for a particular audience;
  2. demonstrate how basic data structures (list, graphs, trees, sets, tables) function;
  3. create simple recursive and iterative algorithms;
  4. evaluate different possible strategies for developing an algorithm and be able to select an appropriate one to solve a given problem;
  5. apply standard patterns to develop algorithms;
  6. break problems down into simpler problems;
  7. determine the complexity of simple algorithms;
  8. recognise 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. Student participation will be assessed during every lecture or tutorial
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.

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
    Students will be presented with information and guided on how to best find solutions for a given problem. 

Assessment Summary

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

Assessment Task Value Due Date
Assignment 1 10% Week 4
Test 15% Week 6 during lecture
Assignment 2 10% Week 10
Student participation 5% Every lecture or weekly.
Examination 1 60% To be advised

Assessment Requirements

Assessment Policy

Assessment Tasks


  • Assessment task 1
    Assignment 1
    This assignment is a series of questions that will aim to help you understand how to go about finding algorithms to solve problems. This assessment relates to Learning Outcomes 1, 2 and 3.
    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. This assessment relates to Learning Outcomes 1, 2, 3, 4, 5 and 6.
    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 during lecture
  • Assessment task 3
    Assignment 2
    This assignment is a series of questions will help you understand different strategies for finding algorithms. This assessment relates to Learning Outcomes 1, 2, 3, 4, 5, 6.
    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
    Student participation
    Student participation will take place during lectures  using clickers. Assessment will be carried out on a weekly basis via Moodle quizes.  This assessment relates to Learning Outcomes 1, 2, 3, 4, 5, 6, 7 and 8.
    Criteria for assessment:

     A grade will be based on a student's quizes via moodle. 

    Due date:
    Every lecture or weekly.


  • Examination 1
    3 hours
    Type (open/closed book):
    Closed book
    Electronic devices allowed in the exam:
    This assessment relates to Learning Outcomes 1, 2, 3, 4, 5, 6, 7 and 8.

Learning resources

Reading list

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

Monash Library Unit Reading List (if applicable to the unit)

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/student-academic-integrity-managing-plagiarism-collusion-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 electronic submission). 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 Edition) Addison-Wesley.

Additional subject costs

Clayton students are required to purchase a Turning Point clicker from the Campus Bookstore or directly from the Australian Distributor. More information about clickers can be found on this webpage: http://intranet.monash.edu.au/infotech/resources/students/enrolment/clickers.html.

Other Information


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: www.policy.monash.edu.au/policy-bank/academic/education/index.html

Faculty resources and policies

Important student resources including Faculty policies are located at http://intranet.monash.edu.au/infotech/resources/students/

Graduate Attributes Policy

Student Charter

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.


Engineers Australia Stage 1 competencies

This unit is a core unit in the Bachelor of Software Engineering accredited by Engineers Australia. Engineers Australia Accreditation Policy of Professional Engineering Programs requires that programs demonstrate how engineering graduates are prepared for entry to the profession and achieve Stage 1 competencies. The following information describes how this unit contributes to the development of these competencies for the Bachelor of Software Engineering. (Note: not all competencies may be emphasised in this unit).

Stage 1 competency How the compency is developed in this unit
 1. Knowledge and Skills base  
 1.1. Comprehension, theory based understanding of the underpinning natural and physical sciences and the engineering fundamentals applicable to the engineering discipline. This is covered in lecture notes, practical exercises and assignments.
 1.2. Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences, which underpin the engineering discipline. This is covered in all assessment components.
 1.3. In-depth understanding of specialist bodies of knowledge within the engineering discipline. Not covered in this unit.
 1.4. Discernment of knowledge development and research directions within th engineering discipline. Not covered in this unit.

 1.5. Knowledge of engineering design practice and contextual factors impacting the engineering discipline.

Not covered in this unit.
 1.6. Understanding of the scope, principles, norms, accountabilities and bounds of sustainable engineering practice in the specific discipline. Not covered in this unit.
 2. Engineering application ability  
 2.1. Application of established engineering methods to complex engineering problem solving. Not covered in this unit.
 2.2 Fluent application of engineering techniques, tools and resources. Not covered in this unit.
 2.3. Application of systematic engineering synthesis and design processes. Not covered in this unit.
 2.4. Application of systematic approaches to the conduct and management of engineering projects. Not covered in this unit.
 3. Professional and personal attributes  
 3.1. Ethical conduct and professional accountability. Not covered in this unit.
 3.2. Effective oral and written communication in professional and lay domains. Students have to express themselves clearly and effectively for their assessment components of the unit.
 3.3. Creative, innovative and proactive demeanour. Some creativity is encouraged to develop solutions for the assessment components.
 3.4. Professional use and management of information. Not covered in this unit.
 3.5. Orderly management of self, and professional conduct. This is covered in the unit through the delivery of solutions to exercises and assignments.
 3.6. Effective team membership and team leadership. Not covered in this unit.

Relationship between Unit Learning Outcomes and BSE Course Outcomes

No. CO 1 CO 2 CO 3 CO 4 CO 5 CO 6 CO 7 C0 8 CO 9 CO 10 CO 11 CO 12 CO 13
 1  X  X            X          
 2  X  X                      
 3  X  X                      
 4  X  X  X                    
 5  X  X  X                    
 6  X  X  X    X                
 7  X  X  X    X                
 8  X  X  X    X  X              

Relationship between Unit Learning Outcomes and Assessments

No. Assignments Tests Practical Exercises Exam
1  X  X  X  X
2  X  X  X  X
3  X    X  X
4  X    X  X
5  X  X  X  X
6  X    X  X
7  X  X  X  X
8  X    X  X
[an error occurred while processing this directive]