ENCN305-17S1 (C) Semester One 2017

Computer Programming and Stochastic Modelling

15 points

Details:
Start Date: Monday, 20 February 2017
End Date: Sunday, 25 June 2017
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Friday, 3 March 2017
  • Without academic penalty (including no fee refund): Friday, 19 May 2017

Description

Programming in Matlab. Exploratory data analysis, model fitting, optimisation, maximum likelihood, residuals analysis, outlier detection, simulation, bootstrap methods.

Computer Programming and Stochastic Modelling is a compulsory 15 point course taught in the first
semester of second professional to all civil and natural resources engineering students. It builds directly on the numerical methods and programming in EMTH171 and on probability and statistical
material taught in EMTH210 and EMTH1118/119. The focus of this course is to further your understanding of applied programming in order to be able to perform calculations and manipulate data in an efficient manner, in particular, datasets which require statistical analysis or stochastic modelling.

The course is split into two broad components, each of which is comprised of a number of sub-topics. The first component covers computer programming, in particular algorithm design, procedural and data abstraction, testing and debugging. These skills will be taught in a MATLAB context but they are equally applicable to most other programming languages. This first component provides the necessary tools for solving problems that arise in the second component on stochastic modelling. In the second component, MATLAB will be used as a means for performing statistical analysis of data. Particular attention will be given to the use of simulation methods (i.e. Monte-Carlo, Bootstrap) for statistical analysis, as opposed to classical asymptotic analysis. You will also examine how datasets can be used to create stochastic models (i.e. Linear regression), and how such models can be used for the prediction of quantities of interest.

The concepts and techniques developed in this course will appear in a number of third professional
courses, in particular all those that require the manipulation or statistical characterisation of experimental or observational data, and the use of observational data to validate/test existing models.

In both components of the course the emphasis is on the application of the programming concepts and stochastic modelling to engineering and civil and natural resources engineering in particular.

Learning Outcomes

  • to improve students’ general programming skills, so they can develop solutions to engineering problems in Matlab and, potentially, in other procedural languages.
  • to give students particular familiarity with the Matlab environment and its relevance to engineering.
  • to introduce Monte Carlo simulation methods for solving statistical problems, in particular: functions of random variables whose input parameters are uncertain, and using bootstrap simulations to perform hypothesis tests to compare datasets with each other or with idealized models.
  • to introduce linear regression as a method to examine dependences between different quantities for which observational data is available.
  • to create stochastic models from observational data and use them for prediction of future observations, or scrutinize observational data with existing models.

Prerequisites

Course Coordinator / Lecturer

Nick Dudley Ward

Lecturers

Richard Lobb and Carl Scarrott

Assessment

Assessment Due Date Percentage 
Assignment 1 (CP) 15%
Assignment 2 (SM) 7%
Assignment 3 (SM) 13%
final exam 40%
On-line Quizzzes (CP) 5%
Test (CP) 20%


The assessment for this paper will comprise four components – on-line quizzes in the computer programming labs, assignments, a mid-semester test and the final exam. All of the material covered in the first component will be assessed in the mid-semester test. The second component will be tested in the final exam.

1. You cannot pass this course unless you achieve a mark of at least 40% in each of the mid-semester test and the final exam. A student who narrowly fails to achieve 40% in either the test or exam, but who performs very well in the other, may be eligible for a pass in the course.

2. All assignments must be submitted by the due date. Late submissions will not be accepted. If a student is unable to complete and submit an assignment by the deadline due to personal circumstances beyond their control they should discuss this with the lecturer involved as soon as possible.

3. Students in this course can apply for aegrotat consideration provided they have sat the mid-term test, the final exam or both.

4. Assignment work for the computer programming section must be done individually. Assignment 3 will be part of the communications portfolio assessment.

Indicative Fees

Domestic fee $919.00

International fee $5,000.00

* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.

For further information see Civil and Natural Resources Engineering .

All ENCN305 Occurrences

  • ENCN305-17S1 (C) Semester One 2017