ENGG7302 - Sem 2 2009 - St Lucia - Internal

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Printed: 24 July 2009, 12:20PM
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1. General Course Information

1.1 Course Details

Course Code: ENGG7302 Course Title: Advanced Computational Techniques in Engineering
Coordinating Unit: School of Information Technology and Electrical Engineering
Semester: Semester 2, 2009    Mode: Internal
Level: Postgraduate Coursework
Location: St Lucia
Number of Units: 2    Contact Hours Per Week: 3L2T
Pre-Requisites: ELEC7005 or ELEC3002
Restrictions: Engineering Postgraduate suite.
Course Description: An advanced course designed to deepen student knowledge and capability in computational techniques in areas of particular importance to engineering. Topics are drawn from linear algebra, stochastic systems and optimisation theory with emphasis on applications and examples in various fields of engineering including but not limited to biomedical engineering, electricity market, embedded systems and microwave & telecommunications. Practical skills in MATLAB programming are developed.
Assumed Background:

A basic undergraduate-level background in engineering mathematics and computational techniques is assumed (such as culminates at UQ in ELEC3002). In particular, it is assumed that the student has a basic understanding of linear algebra, probability & statistics and optimisation, on which the more advanced material in this course will build.

1.2 Course Introduction

The course aims to give students the computational tools and theory needed for postgraduate-level study of engineering. Topics to be covered in the course will be drawn from the following:

  1. Matrix Algebra

    2. Computation of matrix and vector norms, inverses, Gaussian elimination, pivoting, sensitivity, condition number, generalised inverses (Moore-Penrose), idempotent matrices, solution of matrix equations, projection matrices, determinants, cofactors, banded, circulant, Vandermonde & Toeplitz matrices, definiteness, Givens rotations, computation of decompositions (Cholesky, LQ/QR, singular value, eigenvalues/eigenvectors), matrix differentiation.

  2. Stochastic Processes

    3. Continuous- and discrete-time stochastic processes, stationarity, autocorrelation and power spectral density, applications (random walks, point processes, shot noise, cyclostationary processes, bandlimited processes), Fourier series and Karhunen-Loève expansions, ergodicity, estimation, filtering and prediction, Kalman filters, Markov chains, Markov processes, queuing theory.

  3. Optimisation

    4. Linear programming (simplex algorithm, Karmakar's method), unconstrained optimisation (gradient descent, Newton and quasi-Newton's method, conjugate gradient, Levenberg-Marguardt), convex optimisation (Lagrange multipliers, KKT conditions), stochastic and heuristic optimization (random search, simulated annealing, evolutionary algorithms, metaheuristics).

  4. Applications in Engineering

    5. MATLAB as an engineering tool, applications to telecommunications, biomedical engineering, embedded systems, electricity market and other fields of engineering as appropriate.


1.3 Course Staff

Course Coordinator: Dr Marcus Gallagher
Phone: 3365 6197     Email: marcusg@itee.uq.edu.au
Campus: St Lucia Building: Axon Building (Map)   Room: 303
Consultation: Tba at start of semester.

Lecturer: Dr Daniel Angus
Phone: 3346 6359     Email: d.angus@uq.edu.au
Campus: St Lucia Building: Queensland Brain Institute (Map)   Room: 508

Lecturer: Assoc Prof Vaughan Clarkson
Phone: 3365 8834     Email: v.clarkson@uq.edu.au
Campus: St Lucia Building: General Purpose South (Map)   Room: 540
Consultation: 
Mondays at 2pm in Weeks 1-6 and 13, but directly after lectures is also good.


1.4 Timetable

Timetables are available on mySI-net.

2. Aims, Objectives & Graduate Attributes

2.1 Course Aims

The course aims to give students the computational tools and theory needed for postgraduate-level study of engineering. Specifically, the aim is ensure the student has excellent skills in MATLAB for numerical implementation of advanced methods in optimisation, stochastic processes and linear algebra.

2.2 Learning Objectives

After successfully completing this course you should be able to:

1  identify applications in Electrical Engineering where computational techniques in matrix algebra, stochastic systems and optimisation theory can be applied
2  grasp the theoretical foundations of matrix algebra, stochastic systems and optimisation theory
3  understand and explain a number of key algorithms in matrix algebra, stochastic systems and optimisation theory in detail
4  implement computational techniques in MATLAB with a high level of proficiency

2.3. Graduate Attributes

Successfully completing this course will contribute to the recognition of your attainment of the following UQ (Postgrad Coursework) graduate attributes:

GRADUATE ATTRIBUTELEARNING OBJECTIVES
A. IN-DEPTH KNOWLEDGE OF THE FIELD OF STUDY
A2. A broad understanding of the field of study, including how other disciplines relate to the field of study. 1
A3. A comprehensive and in-depth knowledge in the field of study.2, 3, 4
A5. An international perspective on the field of study. 
A7. An appreciation of the link between theory and practice. 
B. EFFECTIVE COMMUNICATION
B1. The ability to collect, analyse and organise information and ideas and to convey those ideas clearly and fluently, in both written and spoken forms.3
B2. The ability to interact effectively with others in order to work towards a common outcome. 
B3. The ability to select and use the appropriate level, style and means of communication.3
B4. The ability to engage effectively and appropriately with information and communication technologies.4
B5. The ability to practise as part of an interdisciplinary team. 
C. INDEPENDENCE AND CREATIVITY
C2. The ability to work and learn independently and effectively.2
C3. The ability to generate ideas and adapt innovatively to changing environments. 
C5. The ability to formulate and investigate problems, create solutions, innovate and improve current practices.1
C6. The abilities and skills that provide a foundation for future leadership roles. 
D. CRITICAL JUDGEMENT
D2. The ability to apply critical reasoning to issues through independent thought and informed judgement.4
D4. The ability to process material and to critically analyse and integrate information from a wide range of sources.1
D5. The ability to evaluate opinions, make decisions and to reflect critically on the justifications for decisions using an evidence-based approach. 
E. ETHICAL AND SOCIAL UNDERSTANDING
E1. An understanding of social and civic responsibility. 
E3. An appreciation of the philosophical and social contexts of a discipline. 
E4. A knowledge and respect of ethics and ethical standards in relation to a major area of study. 
E5. A knowledge of other cultures and times and an appreciation of cultural diversity. 
E7. The ability to work effectively and sensitively across all areas of society. 
E8. An understanding of and respect for the roles and expertise of associated disciplines. 

3. Learning Resources

3.2 Recommended Resources

Gene H. Golub & Charles F. Van Loan, Matrix Computations, Johns Hopkins University Press, 3rd ed., 1996.

 URL
 

Lloyd N. Trefethen & David Bau, III, Numerical Linear Algebra, SIAM, 1997.

 URL
 

Athanasios Papoulis & S. Unnikrishna Pillai, Probability, Random Variables and Stochastic Processes, McGraw-Hill, 4th ed., 2002.

 URL
 

Steven Kay, Intuitive Probability and Random Processes using MATLAB, Springer, 2006.

 URL
 
C. M. Grinstead and J. L. Snell. Introduction to Probability (Ch. 11). URL
 
M. T. Heath. Scientific Computing: An Introductory Survey (Ch. 6). URL
 
James C. Spall.  Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control. URL
 

3.3 University Learning Resources

Access to required and recommended resources, plus past central exam papers, is available at the UQ Library website (http://library.uq.edu.au/search/r?SEARCH=ENGG7302).

The University offers a range of resources and services to support student learning. Details are available on the myServices website (https://student.my.uq.edu.au/).

3.4 School of Information Technology and Electrical Engineering Learning Resources

Students enrolled at St Lucia who wish to retain a hard copy of this profile can use the free print quota provided each semester to students enrolled in courses in the School of Information Technology & Electrical Engineering. For information on how to use this print quota, see the School Policy on Student Photocopying and Printing (St Lucia) (http://www.itee.uq.edu.au/about_ITEE/policies/copy-print.html).

ITEE course websites can be found at http://www.itee.uq.edu.au/~COURSECODE. Many ITEE courses also have Usenet newsgroups, named uq.itee.COURSECODE. Instructions for accessing newsgroups are available at http://studenthelp.itee.uq.edu.au/faq/1stYearFAQ.html#accessnews.

4. Teaching & Learning Activities

4.1 Learning Activities

Date
Activity
Learning Objectives
28 Jul 09
 Introduction (Lecture):
30 Jul 09
 Fundamentals of numerical linear algebra (Lecture Series):
Readings/Ref: Trefethen ; Golub
1, 2, 3, 4
4 Aug 09
 Matrix factorisations and decompositions (Lecture Series):
Readings/Ref: Trefethen ; Golub
1, 2, 3, 4
6 Aug 09
 Matrix factorisations and decompositions (Lecture Series):
Readings/Ref: Trefethen ; Golub
1, 2, 3, 4
11 Aug 09
 Matrix inverses and pseudo-inverses (Lecture Series):
Readings/Ref: Trefethen ; Golub
1, 2, 3, 4
13 Aug 09
 Matrix inverses and pseudo-inverses (Lecture Series):
Readings/Ref: Trefethen ; Golub
1, 2, 3, 4
18 Aug 09
 Special linear systems (Lecture Series):
Readings/Ref: Trefethen ; Golub
1, 2, 3, 4
20 Aug 09
 Special linear systems (Lecture Series):
Readings/Ref: Trefethen ; Golub
1, 2, 3, 4
25 Aug 09
 Stochastic processes (Lecture Series):
Readings/Ref: Kay ; Papoulis
1, 2, 3, 4
27 Aug 09
 Stochastic processes (Lecture Series):
Readings/Ref: Kay ; Papoulis
1, 2, 3, 4
1 Sep 09
 Stochastic processes (Lecture Series):
Readings/Ref: Kay ; Papoulis
1, 2, 3, 4
3 Sep 09
 Stochastic processes (Lecture Series):
Readings/Ref: Kay ; Papoulis
1, 2, 3, 4
8 Sep 09
 Estimation & filtering (Lecture Series):
Readings/Ref: Kay ; Papoulis
1, 2, 3, 4
10 Sep 09
 Estimation & filtering (Lecture Series):
Readings/Ref: Kay ; Papoulis
1, 2, 3, 4
15 Sep 09
 Markov chains (Lecture Series):
Readings/Ref: grinstead ; Kay ; Papoulis
1, 2, 3, 4
17 Sep 09
 Markov chains (Lecture Series):
Readings/Ref: grinstead ; Kay ; Papoulis
1, 2, 3, 4
22 Sep 09
 Optimisation (Lecture Series):
Readings/Ref: heath
1, 2, 3, 4
24 Sep 09
 Optimisation (Lecture Series):
Readings/Ref: heath
1, 2, 3, 4
6 Oct 09
 Optimisation (Lecture Series):
Readings/Ref: heath
1, 2, 3, 4
8 Oct 09
 Optimization (Lecture Series):
Readings/Ref: heath
1, 2, 3, 4
13 Oct 09
 Stochastic optimisation and Metaheuristics (Lecture Series):
Readings/Ref: Spall
1, 2, 3, 4
15 Oct 09
 Stochastic optimisation and Metaheuristics (Lecture Series):
Readings/Ref: Spall
1, 2, 3, 4
20 Oct 09
 Stochastic optimisation and Metaheuristics (Lecture Series):
Readings/Ref: Spall
1, 2, 3, 4
22 Oct 09
 Stochastic optimisation and Metaheuristics (Lecture Series):
Readings/Ref: Spall
1, 2, 3, 4
27 Oct 09
 Course Review (Lecture Series):
1, 2, 3, 4
29 Oct 09
 Exam Preview (Lecture):

5. Assessment

5.1 Assessment Summary

This is a summary of the assessment in the course. For detailed information on each assessment, see 5.5 Assessment Detail below.

Assessment Task
Due Date
Weighting
Learning Objectives
Problem Set/s
Assignment 1
28 Aug 09 17:00
10%
1, 2, 3, 4
In Class Quiz
Class Test
1 Sep 09
10%
1, 2, 3
Exam - Mid Semester During Class
Mid-semester exam
22 Sep 09
10%
1, 2, 3
Problem Set/s
Assignment 2
25 Sep 09 17:00
10%
1, 2, 3, 4
Problem Set/s
Assignment 3
30 Oct 09 17:00
10%
1, 2, 3, 4
Exam - during Exam Period (Central)
Final exam
Examination Period
50%
1, 2, 3

5.2 Course Grading


Grade 1, Fail: Fails to demonstrate most or all of the basic requirements of the course: This grade is awarded to students who achieve less than 20% of the total marks.

Grade 2, Fail: Demonstrates clear deficiencies in understanding and applying fundamental concepts; communicates information or ideas in ways that are frequently incomplete or confusing and give little attention to the conventions of the discipline: This grade is awarded to students who achieve between 20% and 44% of the total marks.

Grade 3, Fail: Demonstrates superficial or partial or faulty understanding of the fundamental concepts of the field of study and limited ability to apply these concepts; presents undeveloped or inappropriate or unsupported arguments; communicates information or ideas with lack of clarity and inconsistent adherence to the conventions of the discipline: This grade is awarded to students who achieve between 45% and 49% of the total marks.

Grade 4, Pass: Demonstrates adequate understanding and application of the fundamental concepts of the field of study; develops routine arguments or decisions and provides acceptable justification; communicates information and ideas adequately in terms of the conventions of the discipline: This grade is awarded to students who achieve between 50% and 64% of the total marks.

Grade 5, Credit: Demonstrates substantial understanding of fundamental concepts of the field of study and ability to apply these concepts in a variety of contexts; develops or adapts convincing arguments and provides coherent justification; communicates information and ideas clearly and fluently in terms of the conventions of the discipline: This grade is awarded to students who achieve between 65% and 74% of the total marks.

Grade 6, Distinction: As for 5, with frequent evidence of originality in defining and analysing issues or problems and in creating solutions; uses a level, style and means of communication appropriate to the discipline and the audience: This grade is awarded to students who achieve between 75% and 84% of the total marks.

Grade 7, High Distinction: As for 6, with consistent evidence of substantial originality and insight in identifying, generating and communicating competing arguments, perspectives or problem solving approaches; critically evaluates problems, their solutions and implications: This grade is awarded to students who achieve 85% or more of the total marks.

Other Requirements & Comments : At the discretion of the coordinator, final grades may be scaled upwards but not decreased.

5.3 Late Submission

Late submission of the assignment will result in a penalty of 10% per day.  Submissions more than 5 days late will not be accepted.  A late submission should be made to the School Office, not the submission boxes.

No extensions will be granted except in exceptional personal circumstances (documented medical reason or family emergency). Personal hardware or computer failures are not grounds for extension.

5.5 Assessment Detail

Assignment 1
Type: Problem Set/s
Learning Objectives Assessed: 1, 2, 3, 4
Due Date:
         28 Aug 09 17:00
Weight: 10%
Task Description:

There will be three assignments, due at four-weekly intervals from the end of Week 5. The assignments will be designed to develop skills in problem solving based on the theory presented in lectures, often using MATLAB.


Criteria & Marking:

The assignments will be marked using a scheme based on the following criteria:

  • demonstrated understanding and application of theory
  • correct approach to problem solution
  • operation of MATLAB code

 


Class Test
Type: In Class Quiz
Learning Objectives Assessed: 1, 2, 3
Due Date:
         1 Sep 09
Weight: 10%
Perusal: 5 minutes
Duration: 40 minutes
Format: Multiple-choice
Task Description:

This multiple-choice test will cover material from Weeks 1-5. It will be held durial the tutorial session in Week 6.


Criteria & Marking:

Full marks will be awarded for correct answers, no marks otherwise.


Mid-semester exam
Type: Exam - Mid Semester During Class
Learning Objectives Assessed: 1, 2, 3
Due Date:
         22 Sep 09
Weight: 10%
Perusal: 5 minutes
Duration: 40 minutes
Format: Multiple-choice
Task Description:

This multiple-choice test will cover material from Weeks 6-9. It will be held durial the tutorial session in Week 10.


Criteria & Marking:

Full marks will be awarded for correct answers, no marks otherwise.


Assignment 2
Type: Problem Set/s
Learning Objectives Assessed: 1, 2, 3, 4
Due Date:
         25 Sep 09 17:00
Weight: 10%
Task Description: There will be three assignments, due at four-weekly intervals from the end of Week 5. The assignments will be designed to develop skills in problem solving based on the theory presented in lectures, often using MATLAB.
Criteria & Marking:

The assignments will be marked using a scheme based on the following criteria:

  • demonstrated understanding and application of theory
  • correct approach to problem solution
  • operation of MATLAB code

Assignment 3
Type: Problem Set/s
Learning Objectives Assessed: 1, 2, 3, 4
Due Date:
         30 Oct 09 17:00
Weight: 10%
Task Description: There will be three assignments, due at four-weekly intervals from the end of Week 5. The assignments will be designed to develop skills in problem solving based on the theory presented in lectures, often using MATLAB.
Criteria & Marking:

The assignments will be marked using a scheme based on the following criteria:

  • demonstrated understanding and application of theory
  • correct approach to problem solution
  • operation of MATLAB code

Final exam
Type: Exam - during Exam Period (Central)
Learning Objectives Assessed: 1, 2, 3
Due Date:
         Examination Period
Weight: 50%
Perusal: 10 minutes
Duration: 180 minutes
Format: Short answer, Problem solving
Task Description:

The end of semester exam will be based on material presented/covered throughout the semester, but with an emphasis on the material in Weeks 10-13. The questions will involve problem solving.

The exam will be three hours in duration and will be held during the final examination period. This exam will be closed-book and will contain short-answer, and problem solving questions. You may also bring a battery-operated non-programmable calculator. Programmable calculators and other computing or communication devices are not permitted.

Note: Assessment variation is possible for students with a disability.

Note: Students may request the use of dictionaries, including bilingual dictionaries, supplied by Examinations Section.


Criteria & Marking:

Broadly speaking, the criteria used for marking the final exam are, according to the type of question asked, as follows:

  • knowledge of basic definitions of terms used in the course,
  • clear understanding and explanation of the theory and methods,
  • demonstrated ability to correctly solve computational problems.

 


6. Policies & Guidelines

 
This section contains the details of and links to the most relevant policies and course guidelines. For further details on University Policies please visit myAdvisor and the University Handbook of Policies and Procedures.

6.1 Assessment Related Policies and Guidelines

University Policies & Guidelines

An overview of the University’s assessment-related policies can be found on myAdvisor (http://www.uq.edu.au/myadvisor/index.html?page=2910).

Academic Integrity
It is the University's task to encourage ethical scholarship and to inform students and staff about the institutional standards of academic behaviour expected of them in learning, teaching and research. Students have a responsibility to maintain the highest standards of academic integrity in their work. Students must not cheat in examinations or other forms of assessment and must ensure they do not plagiarise.

Plagiarism
The University has adopted the following definition of plagiarism:

Plagiarism is the act of misrepresenting as one's own original work the ideas, interpretations, words or creative works of another. These include published and unpublished documents, designs, music, sounds, images, photographs, computer codes and ideas gained through working in a group. These ideas, interpretations, words or works may be found in print and/or electronic media.

Students are encouraged to read the UQ Academic Integrity and Plagiarism policy (http://www.uq.edu.au/hupp/index.html?page=25128) which makes a comprehensive statement about the University's approach to plagiarism, including the approved use of plagiarism detection software, the consequences of plagiarism and the principles associated with preventing plagiarism.

Feedback on Assessment
Feedback is essential to effective learning and students can expect to receive appropriate and timely feedback on all assessment. For a detailed explanation of the feedback you are entitled to, you should consult the policy on Student Access to Feedback on Assessment. (http://www.uq.edu.au/hupp/index.html?page=25109)

As a student you have a responsibility to incorporate feedback into your learning; make use of the assessment criteria that you are given; be aware of the rules, policies and other documents related to assessment; and provide teachers with feedback on their assessment practices.

There are certain steps you can take if you feel your result does not reflect your performance. Please refer to the myAdvisor web site. (http://www.uq.edu.au/myadvisor/index.html?page=2953)

School of Information Technology and Electrical Engineering Assessment Guidelines

Misconduct

Further to the statement on academic integrity and plagiarism above, students are required to read and understand the ITEE policy on Student Misconduct (http://www.itee.uq.edu.au/about_ITEE/policies/student-misconduct.html).

Late Arrival or Non-attendance at Examinations

The policy and procedure for late arrival or non-attendance at centrally controlled and school-based examinations is set out in the University's Assessment policy (HUPP 3.30.1), section 4.8 at http://www.uq.edu.au/hupp/index.html?page=25109.

In the case that a student requests a special exam for a School-controlled exam, the request will be considered and, if allowed, the timing shall be determined by the course coordinator, in consultation with the School's Chief Examiner where necessary, and in accordance with HUPP 3.30.1. Unless otherwise indicated in the Course Profile, applications must be made in writing to the Head of School no later than 5 days after the exam. Late applications will not be accepted.
 
Examination Feedback
 
In addition to the advice above, students wishing to view examination answer scripts and/or question papers should consult with the School office (Room 217, General Purpose South Building [78], St Lucia) regarding arrangements. The ITEE policy on exam script viewing is available at http://study.itee.uq.edu.au/current_students/exam_script_viewing.html.

Supplementary Assessment

If you fail this course you may be eligible for supplementary assessment - see the general award rules and/or your program rules for details. You should note that even though you may be eligible for supplementary assessment under these rules, in some circumstances there may be no practical assessment that can be offered to allow you to meet the minimum passing requirements. These circumstances may include failure based on:
  • group or team based assessment;
  • attendance or class participation requirements;
  • laboratory-based assessment, where laboratories can't practically be made available after classes have finished;
  • project or thesis-based assessment, where a significant period of time would be required to undertake supplementary assessment;
  • progressive assessment, where subsequent assessment items build on earlier assessment items; or
  • multiple assessment items, where it is impractical to offer multiple supplementary assessment items.
If the course coordinator determines that there is no practical supplementary assessment that can be offered to allow you to improve your grade, then you will not be offered supplementary assessment and your grade will remain unchanged.


Calculators in Examinations

Some examinations in the School of Information Technology and Electrical Engineering restrict the type of calculator that can be used. If this course profile does not specify any calculator restrictions, you should check with the course coordinator as to whether any restrictions apply. In some examinations, you may only be permitted to use an EPSA/EAIT approved and labelled non-programmable calculator. It is your responsibility to ensure you have a suitable approved and labelled calculator if required.

6.2 Other Policies and Guidelines

University Policies and Guidelines

Placement Courses
Students on a placement course – also known as a work placement, internship, industry study, industry experience, clinical practice, clinical placement, practical work, practicum, fieldwork, teaching practice – should refer to the University policy, Placement Courses (http://www.uq.edu.au/hupp/index.html?page=25120) for detailed information.
 
Working with Children
Students whose studies include a professional/work placement, internship, clinical practice, teaching practice or other similar activity which involves them in regular contact with children should refer to the University policy, Working with Children Check - "blue card" (http://www.uq.edu.au/hupp/index.html?page=25004) to find out how to apply for a ‘blue card’.
 
Students with a Disability
Any student with a disability who may require alternative academic arrangements, including assessment, in the course/program is encouraged to seek advice at the commencement of the semester from a Disability Adviser at Student Support Services. Refer to the University policy, Students with a Disability (Disability Action Plan) (http://www.uq.edu.au/hupp/index.html?page=25122) and to the policy on Special Arrangements for Examinations for Students with a Disability (http://www.uq.edu.au/hupp/index.html?page=25111

Where an adjustment is made to an accredited program, it is the responsibility of the relevant Faculty to liaise with professional and registration bodies regarding the acceptability of the change/s.  

Occupational Health and Safety
Undergraduate Students (http://www.uq.edu.au/hupp/index.html?page=25055) and Postgraduate Students (http://www.uq.edu.au/hupp/index.html?page=25057) should be familiar with the University policies on occupational health and safety in the laboratory.

Other School of Information Technology and Electrical Engineering Guidelines

Ethical Clearance
If your course involves assignment or project work involving human subjects or human-related materials, you must investigate the need for ethical clearance and obtain it when required. Information on ethical clearance can be found at http://www.uq.edu.au/research/orps/index.html?page=5064&pid=5256.

Learning Summary

 

Below is a table showing the relationship between the learning objectives for this course and the broader graduate attributes developed, the learning activities used to develop each objective and the assessment task used to assess each objective.

Learning Objectives

After successfully completing this course you should be able to:

1  identify applications in Electrical Engineering where computational techniques in matrix algebra, stochastic systems and optimisation theory can be applied
2  grasp the theoretical foundations of matrix algebra, stochastic systems and optimisation theory
3  understand and explain a number of key algorithms in matrix algebra, stochastic systems and optimisation theory in detail
4  implement computational techniques in MATLAB with a high level of proficiency


Assessment & Learning Activities

  Learning Objectives
  1 2 3 4
Learning Activities
Introduction (Lecture)        
Fundamentals of numerical linear algebra (Lecture Series)
selected
selected
selected
selected
Matrix factorisations and decompositions (Lecture Series)
selected
selected
selected
selected
Matrix factorisations and decompositions (Lecture Series)
selected
selected
selected
selected
Matrix inverses and pseudo-inverses (Lecture Series)
selected
selected
selected
selected
Matrix inverses and pseudo-inverses (Lecture Series)
selected
selected
selected
selected
Special linear systems (Lecture Series)
selected
selected
selected
selected
Special linear systems (Lecture Series)
selected
selected
selected
selected
Stochastic processes (Lecture Series)
selected
selected
selected
selected
Stochastic processes (Lecture Series)
selected
selected
selected
selected
Stochastic processes (Lecture Series)
selected
selected
selected
selected
Stochastic processes (Lecture Series)
selected
selected
selected
selected
Estimation & filtering (Lecture Series)
selected
selected
selected
selected
Estimation & filtering (Lecture Series)
selected
selected
selected
selected
Markov chains (Lecture Series)
selected
selected
selected
selected
Markov chains (Lecture Series)
selected
selected
selected
selected
Optimisation (Lecture Series)
selected
selected
selected
selected
Optimisation (Lecture Series)
selected
selected
selected
selected
Optimisation (Lecture Series)
selected
selected
selected
selected
Optimization (Lecture Series)
selected
selected
selected
selected
Stochastic optimisation and Metaheuristics (Lecture Series)
selected
selected
selected
selected
Stochastic optimisation and Metaheuristics (Lecture Series)
selected
selected
selected
selected
Stochastic optimisation and Metaheuristics (Lecture Series)
selected
selected
selected
selected
Stochastic optimisation and Metaheuristics (Lecture Series)
selected
selected
selected
selected
Course Review (Lecture Series)
selected
selected
selected
selected
Exam Preview (Lecture)        
Assessment Tasks
Assignment 1
selected
selected
selected
selected
Class Test
selected
selected
selected
  
Mid-semester exam
selected
selected
selected
  
Assignment 2
selected
selected
selected
selected
Assignment 3
selected
selected
selected
selected
Final exam
selected
selected
selected
  

Graduate Attributes

Successfully completing this course will contribute to the recognition of your attainment of the following UQ (Postgrad Coursework) graduate attributes:

  Learning Objectives
  1 2 3 4
Graduate Attributes
A IN-DEPTH KNOWLEDGE OF THE FIELD OF STUDY
A2. A broad understanding of the field of study, including how other disciplines relate to the field of study.
selected
      
A3. A comprehensive and in-depth knowledge in the field of study.  
selected
selected
selected
A5. An international perspective on the field of study.        
A7. An appreciation of the link between theory and practice.        
B EFFECTIVE COMMUNICATION
B1. The ability to collect, analyse and organise information and ideas and to convey those ideas clearly and fluently, in both written and spoken forms.    
selected
  
B2. The ability to interact effectively with others in order to work towards a common outcome.        
B3. The ability to select and use the appropriate level, style and means of communication.    
selected
  
B4. The ability to engage effectively and appropriately with information and communication technologies.      
selected
B5. The ability to practise as part of an interdisciplinary team.        
C INDEPENDENCE AND CREATIVITY
C2. The ability to work and learn independently and effectively.  
selected
    
C3. The ability to generate ideas and adapt innovatively to changing environments.        
C5. The ability to formulate and investigate problems, create solutions, innovate and improve current practices.
selected
      
C6. The abilities and skills that provide a foundation for future leadership roles.        
D CRITICAL JUDGEMENT
D2. The ability to apply critical reasoning to issues through independent thought and informed judgement.      
selected
D4. The ability to process material and to critically analyse and integrate information from a wide range of sources.
selected
      
D5. The ability to evaluate opinions, make decisions and to reflect critically on the justifications for decisions using an evidence-based approach.        
E ETHICAL AND SOCIAL UNDERSTANDING
E1. An understanding of social and civic responsibility.        
E3. An appreciation of the philosophical and social contexts of a discipline.        
E4. A knowledge and respect of ethics and ethical standards in relation to a major area of study.        
E5. A knowledge of other cultures and times and an appreciation of cultural diversity.        
E7. The ability to work effectively and sensitively across all areas of society.        
E8. An understanding of and respect for the roles and expertise of associated disciplines.        

Authorised by: Deputy Vice-Chancellor (Academic)
Maintained by: Software Services
Last Updated - 24 May , 2006