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AMTH250 Introduction to Scientific Computation

Updated: 27 March 2013
Credit Points 6
Offering
Responsible Campus Teaching Period Mode of Study
Armidale Trimester 2 On line
Armidale Trimester 2 On Campus
Intensive School(s) None
Supervised Exam There is no UNE Supervised Examination.
Pre-requisites MATH102 or MATH120 or candidature in a postgraduate award in the School of Environmental and Rural Science or School of Science and Technology
Co-requisites None
Restrictions AMTH142 or AMTH247
Notes None
Combined Units None
Coordinator(s) Ioan Despi (idespi@une.edu.au)
Unit Description

A unit introducing students to numerical and computational methods in applied mathematics. The use of software packages such as Scilab and Maxima is taught in order to provide fundamental computational tools for numerical approximation, symbolic manipulation and graphical analysis. Topics include numerical differentiation and integration as well as solving systems of linear equations, alongside basic mathematical principles of error and estimation of computational accuracy. Further topics include principles of random number generation, and iterative schemes for equation-solving.
Materials Textbook information will be displayed approximately 8 weeks prior to the commencement of the teaching period. Please note that textbook requirements may vary from one teaching period to the next.
Disclaimer Unit information may be subject to change prior to commencement of the teaching period.
Assessment Assessment information will be published prior to commencement of the teaching period.
Learning Outcomes (LO) Upon completion of this unit, students will be able to:
  1. demonstrate proficiency in use of software packages such as Scilab and Maxima for numerical approximation, symbolic manipulation and graphical analysis;
  2. understand their application to a range of topics in computational mathematics;
  3. understand the basic principles of computational error and limits of accuracy;
  4. demonstrate ability to apply computational techniques to analysis of real-world quantitative problems.

Graduate Attributes (GA)
Attribute Taught Assessed Practised
1 Knowledge of a Discipline
Knowledge gained by the student in lectures will be applied in collaboration with the lecturer to problems and examples in tutorials. The student will then map this experience onto further problem-solving tasks in assignments, where the identification of central concepts in the discipline, and the student's ability to articulate them, will be assessed. 		True True True
2 Communication Skills
The student will be encouraged to participate actively in discussion during lectures and tutorials. Written communication skills, particularly with regard to construction and presentation of logical expositions and arguments, will be taught and assessed. 		True True True
6 Problem Solving
The student will encounter in this unit a field of knowledge that is intensely problem-based, and will acquire skill in connecting ideas within a network of logical relationships. A high emphasis will be placed on the development of analytical and deductive reasoning as applied to the limits of computer modelling. 		True True True
   

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