Course Title: Mathematical Computing and Algorithms
Part A: Course Overview
Course Title: Mathematical Computing and Algorithms
Credit Points: 12.00
Terms
Course Code |
Campus |
Career |
School |
Learning Mode |
Teaching Period(s) |
|
MATH2109 |
City Campus |
Undergraduate |
145H Mathematical & Geospatial Sciences |
Face-to-Face | Sem 1 2006, Sem 1 2007, Sem 1 2008, Sem 1 2009, Sem 1 2010, Sem 1 2011, Sem 1 2012, Sem 1 2013, Sem 1 2014, Sem 1 2015, Sem 1 2016 |
|
MATH2109 |
City Campus |
Undergraduate |
171H School of Science |
Face-to-Face | Sem 1 2017, Sem 1 2018, Sem 1 2019, Sem 1 2020 |
Course Coordinator: Assoc Professor Joseph Apaloo
Course Coordinator Phone: N/A
Course Coordinator Email: joseph.apaloo@rmit.edu.au
Pre-requisite Courses and Assumed Knowledge and Capabilities
None
Course Description
This course is designed to teach you how to use a mathematical programming language (package) such as MATLAB, Maple or Mathematica to construct computer models of mathematical and physical problems. MATLAB, Maple and Mathematica differ from a language like C in that they have extensive sets of routines for performing standard mathematical calculations. MATLAB, Maple and Mathematica are three of the standard computing packages used by mathematicians and knowledge of such packages is usually assumed by an employer. The skills you gain in this course will also be used in many of the other mathematics and statistics courses in your program. Skills will be developed through individual modelling exercises in the lab following on from appropriate lecture material.
Objectives/Learning Outcomes/Capability Development
This course contributes to the following Program Learning Outcomes for BP083 Bachelor of Science (Mathematics) and BP245 Bachelor of Science (Statistics):
Knowledge and technical competence
- the ability to use the appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.
Problem-solving
- the ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
- an understanding of the balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.
On completion of this course you should be able to:
- Write and debug small computer programs
- Model and / or solve a range of mathematical problems using either the in-built functions or by writing your own computer program.
- Interpret and report on the results obtained.
Overview of Learning Activities
You will attend lectures where theory will be progressively presented. Computer laboratory sessions are also scheduled in which individual skills components will be practised and you can seek one-on-one assistance. In addition problem based assignments will enable the integration of the component skills.
Overview of Learning Resources
You have access to the computer laboratory, program documentation, external internet links and access to RMIT Library online and hardcopy resources.
Lab and assignment sheets will be provided as well as access to the same sheets in electronic form on Canvas.
Students may also find the following Library guide useful: http://rmit.libguides.com/mathstats
Overview of Assessment
Assessment tasks
Early Assessment Task: Hand in Labs (Weeks 2 to 11 inclusive)
Weighting 10%
This assessment task supports CLOs 1 & 2
Assessment Task 2: Assignment 1(take home) (Weeks 6 to 8)
Weighting 15%
This assessment task supports CLOs 1 & 2
Assessment Task 3: Assignment 2 (take home) (Weeks 10 to 12)
Weighting 15%
This assessment task supports CLO 1 & 2
Assessment 4: Midsemester Test(written)
Weighting 10%
This assessment supports CLOs 1, 2 & 3
Assessment 5: Final Exam(written)
Weighting 50%
This assessment supports CLOs 1, 2 & 3