Course Title: Algorithms and Analysis

Part A: Course Overview

Course Title: Algorithms and Analysis

Credit Points: 12.00

Terms

Course Code	Campus	Career	School	Learning Mode	Teaching Period(s)
COSC1285	City Campus	Postgraduate	140H Computer Science & Information Technology	Face-to-Face	Sem 1 2006, Sem 2 2006, Summer2007, Sem 1 2007, Sem 2 2007, Summer2008, Sem 1 2008, Sem 2 2008, Summer2009, Sem 1 2009, Sem 2 2009, Sem 1 2010, Sem 2 2010, Summer2011, Sem 1 2011, Sem 2 2011, Summer2012, Sem 1 2012, Sem 2 2012, Sem 2 2013, Summer2014, Sem 2 2014, Summer2015, Sem 2 2015, Sem 1 2016
COSC1285	City Campus	Postgraduate	171H School of Science	Face-to-Face	Sem 1 2017, Sem 2 2017, Sem 1 2018, Sem 2 2018, Sem 1 2019, Sem 2 2019, Sem 1 2020, Sem 2 2020, Sem 1 2021, Sem 2 2021
COSC1285	City Campus	Postgraduate	175H Computing Technologies	Face-to-Face	Sem 2 2022
COSC2123	City Campus	Undergraduate	140H Computer Science & Information Technology	Face-to-Face	Sem 1 2006, Sem 2 2006, Summer2007, Sem 1 2007, Sem 2 2007, Summer2008, Sem 1 2008, Sem 2 2008, Summer2009, Sem 1 2009, Sem 2 2009, Sem 1 2010, Sem 2 2010, Summer2011, Sem 1 2011, Sem 2 2011, Summer2012, Sem 1 2012, Sem 2 2012, Sem 2 2013, Summer2014, Sem 2 2014, Summer2015, Sem 2 2015, Sem 1 2016
COSC2123	City Campus	Undergraduate	171H School of Science	Face-to-Face	Sem 1 2017, Sem 2 2017, Sem 1 2018, Sem 2 2018, Sem 1 2019, Sem 2 2019, Sem 1 2020, Sem 2 2020, Sem 1 2021, Sem 2 2021
COSC2123	City Campus	Undergraduate	175H Computing Technologies	Face-to-Face	Sem 2 2022
COSC2203	RMIT University Vietnam	Postgraduate	175H Computing Technologies	Face-to-Face	Viet3 2022
COSC2469	RMIT University Vietnam	Undergraduate	140H Computer Science & Information Technology	Face-to-Face	Viet2 2016
COSC2469	RMIT University Vietnam	Undergraduate	171H School of Science	Face-to-Face	Viet2 2017, Viet3 2020, Viet3 2021
COSC2469	RMIT University Vietnam	Undergraduate	175H Computing Technologies	Face-to-Face	Viet1 2022, Viet3 2022
COSC2498	Taylors College KL	Undergraduate	140H Computer Science & Information Technology	Face-to-Face	Offsh 3 10

Course Coordinator: Associate Professor Jeffrey Chan

Course Coordinator Phone: +613 9925 5270

Course Coordinator Email: jeffrey.chan@rmit.edu.au

Course Coordinator Availability: By appointment

Pre-requisite Courses and Assumed Knowledge and Capabilities

Enforced Pre-requisite Course

For the postgraduate course offering:

Successful completion of:

COSC2531 - Programming Fundamentals (Course ID 045682)

 

For the undergraduate course offering:

Successful completion of:

COSC2288 / COSC2391 / COSC2440 / COSC2684 / COSC2786 - Further Programming (Course ID 014052)

OR

COSC2802 - Programming Bootcamp 2 (Course ID 054080)

OR

COSC1076 / COSC2082 / COSC2136 / COSC2696 - Advanced Programming Techniques (Course ID 004068)

OR

COSC2800 -  IT Studio 2 (Course ID 054075)

 

Note: it is a condition of enrolment at RMIT that you accept responsibility for ensuring that you have completed the prerequisite/s and agree to concurrently enrol in co-requisite courses before enrolling in a course.

For your information go to RMIT Course Requisites webpage.

Course Description

The main objective of this course is for you to acquire the tools and techniques necessary to propose practical algorithmic solutions to real-world problems which still allow strong theoretical bounds on time and space usage. You will study a broad variety of important and useful algorithms and data structures in different areas of applications, and will concentrate on fundamental algorithms. You will spend a significant time on each algorithm to understand its essential characteristics and to respect its subtleties.

Objectives/Learning Outcomes/Capability Development

Program Learning Outcomes

This course contributes to the following Program Learning Outcomes (PLOs) for BP094 Bachelor of Computer Science, BP096 Bachelor of Software Engineering, BP215 Bachelor of Information Technology (Games and Graphics Programming), BH094 Bachelor of Engineering (Electronic & Communication Engineering (Hons)/Bachelor of Computer Science, BH091 - Bachelor of Engineering (Computer and Network Engineering) (Honours)/Bachelor of Computer Science, MC061 Master of Computer Science, and MC208 Master of Information Technology:

• Enabling Knowledge: You will gain skills as you apply knowledge effectively in diverse contexts.
• Enabling Knowledge: You will gain skills as you apply knowledge with creativity and initiative to new situations. In doing so, you will: Demonstrate mastery of a body of knowledge that includes recent developments in computer science and information technology; Recognise and use research principles and methods applicable to computer science and information technology.

• Critical Analysis: You will learn to accurately and objectively examine and consider computer science and information technology (IT) topics, evidence, or situations, in particular to: (i) Analyse and model requirements and constraints for the purpose of designing and implementing software artefacts and IT systems; (ii) Evaluate and compare designs of software artefacts and IT systems on the basis of organisational and user requirements.

• Problem Solving: Your capability to analyse problems and synthesise suitable solutions will be extended as you learn to: Design and implement software solutions that accommodate specified requirements and constraints, based on analysis or modelling or requirements specification. 

• Communication: You will learn to communicate effectively with a variety of audiences through a range of modes and media, in particular to: Present a clear, coherent and independent exposition of software applications, alternative IT solutions, and decision recommendations to both IT and non-IT personnel via technical reports of professional standard and technical presentations. Interpret abstract theoretical propositions, choose methodologies, justify conclusions and defend professional decisions to both IT and non-IT personnel via technical reports of professional standard and technical presentations.

• Team Work: You will learn to work as an effective and productive team member in a range of professional and social situations, in particular to: Work effectively in different roles, to form, manage, and successfully produce outcomes from teams whose members may have diverse cultural backgrounds and life circumstances and differing levels of technical expertise. 

 

 

On successful completion of this course you should be able to:

1. Compare, contrast, and apply the key algorithmic design paradigms: brute force, divide and conquer, decrease and conquer, transform and conquer, greedy, dynamic programming and iterative improvement;
2. Compare, contrast, and apply key data structures: trees, lists, stacks, queues, hash tables and graph representations;
3. Define, compare, analyse, and solve general algorithmic problem types: sorting, searching, graphs and geometric;
4. Theoretically compare and analyse the time complexities of algorithms and data structures; and 
5. Implement, empirically compare, and apply fundamental algorithms and data structures to real-world problems.    

Overview of Learning Activities

Key concepts will be explained in pre-recorded lectures and live lectorials, where course material will be presented and the subject matter will be illustrated with demonstrations and examples.

Online workshop sessions focus on problem solving and application of data structures and algorithms learnt in classes and provide practice in the application of theory and procedures.

Overview of Learning Resources

Computer laboratories and relevant software will be provided by the School. You will be able to access course information and learning materials and any recommended textbooks through the Canvas learning management system.  Lists of relevant reference texts, resources in the library and freely accessible Internet sites will be provided.   

Overview of Assessment

The assessment for this course comprises of assignments, weekly quizzes and a set of end-of-semester, take-home exercises.
This course has no hurdle requirements.

Assessment Task 1: Weekly Quizzes

Weighting 10%  

This assessment task supports CLOs 1, 2, 3 & 4

Assessment Task 2: Assignment 1

Weighting 30%

This assessment task supports CLOs 1, 2, 3, 4 & 5

Assessment Task 3: Assignment 2

Weighting 40%

This assessment supports CLOs 1, 2, 3, 4 & 5

Assessment Task 4: End-of-semester Take-home Exercises

Weighting 20%

This assessment supports CLOs 1, 2, 3, 4 & 5

Please note that postgraduate students are expected to demonstrate deeper knowledge and higher level application of knowledge and skills than undergraduate students. There may be a postgraduate and an undergraduate version for end-of-semester take-home exercises.