This is version 1.0 of the COGS2010course profile, dated 31st July 2003
Not applicable.
| Course Code(s): | COGS2010 |
| Unit Value: | #2 |
| Contact Hours: | 4 hours per week (2L2P) |
| Purpose: | COGS2010 introduces basic principles and issues related to computational modeling of cognitive, perceptual and linguistics processes in cognitive science. It introduces neural networks and other modeling frameworks, and emphasizes the significance of modeling techniques for cognitive science. |
Dr Janet Wiles (Course Coordinator)
Office:
78-333
Phone:
52902
Fax: (07) 3365-4999
Email:
janetw@itee.uq.edu.au
Consultation Time: 2-4pm Tuesdays
Note: If you are calling from outside the University follow the appropriate instructions for each location below.
| University of Queensland (St Lucia) indial |
(07) 336 5xxxx or (07) 334 6xxxx |
| Ipswich Campus indial | (07) 338 1xxxx |
Ben Skellett
School of Information Technology and Electrical Engineering
Room: 430, General Purpose South Building (bldg 78)
Email: ben @ itee.uq.edu.au
Phone: 3365 2904
Office hours: Thursday 2-4
Cogs2010 includes two different kinds of knowledge, content and skill.
During the course students should:
acquire knowledge and understanding of connectionist and other computational models that are used to model cognition and intelligence.
gain an understanding of the cognitive phenomena to which these architectures apply.
gain an appreciation of fundamental cognitive issues that these architectures highlight.
acquire the skills to simulate these architectures.
The University of Queensland has defined a set of graduate attributes to specify broad core knowledge and skills associated with all undergraduate programs (http://www.uq.edu.au/hupp/contents/view.asp?s1=3&s2=20&s3=5). This course addresses these attributes as follows:
| Attribute | Contributions from this Course |
|---|---|
| In-depth knowledge of the field of study | This course should contribute to the student’s growing knowledge of cognitive science, how it is studied and how it relates to other disciplines. It introduces specific skills in simulation of cognitive phenomena. |
| Effective Communication | Practice in effective communication is fostered through discussions in lectures and tutorials, and through written assignments. |
| Independence and Creativity | The assignment provides an opportunity to explore an area of the student's own interest in cognitive science, with their own independent research. |
| Critical Judgement | Evaluation of models in the tutorials fosters critical thinking. |
| Ethical and Social Understanding | Understanding issues in cross disciplinary study. |
Students are assumed to have completed at least two units of computer science or information technology (such as COMP1500), 2 units of mathematics or statistics (such as MATH, or PSYC1040) and 6 units of advanced courses (second year or above) related to cognitive science (such as COMP, PSYC, PHIL, LING). It is also recommended that students have completed COGS1000 or COGS2000 or an introductory artificial intelligence or cognitive science course.
In the first lecture (or class meeting) students will be directed to the web address at which this course profile can be read. Students enrolled at St Lucia who wish to retain a hard copy of the 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). Students enrolled at the Ipswich campus will either be provided with a hard copy or given directions in class on how to obtain a free copy.
The lab sessions will involve using several simulation packages. The neural network exercises are from the online textbook “Connectionist Models of Cognition”. You don’t need to register to use Brainwave in the lab. The local site for Brainwave is at http://www.itee.uq.edu.au/~cogs2010/cmc/
Hyperlinks to the appropriate chapters are provided in the schedule table. The textbook contains interactive exercises using the Brainwave neural network simulator, which is built into the web pages. We recommend that you use Internet Explorer 4.0 or greater on Windows 95/98 for best results. To access the textbook outside of the laboratory times, you can use the library machines or use your own Internet connection.
None required.
Handouts and copies of lectures will be available from the course web page.
Computer laboratories will be used. See ITEE Student Guide for Occupational Health and Safety requirements.
Office hours:
Janet Wiles: Tuesday 2-4
Ben Skellett: Thursday 2-4
Notices will be posted on the course web page.
The course web site is available at http://www.itee.uq.edu.au/~cogs2010. The course web site will contain announcements and links to the profile.
The course newsgroup is uq.itee.cogs2010. This group is available on both the University and School news servers (news.uq.edu.au and news.itee.uq.edu.au).
Students are free to post questions (and answers!) to the newsgroup. Copies of announcements will also be posted to the newsgroup. The teaching staff will monitor the newsgroup occasionally, but the forum is primarily for student discussions about cogs2010 and cognitive science issues.
There is one practical session each week: Wednesday 2-4pm (room 78-208). Sign up is not required.
Pracs start in week 2. Note that tutorial material is assessed and must be
submitted before the prac if you do not attend.
You are not required to attend any of the teaching sessions (except those in which an assessment activity is taking place), however, you are strongly encouraged to do so. The lectures and pracs have been specifically designed to aid your learning of the course material. Failure to attend a session may result in you being disadvantaged. It is up to you to find out what happened at any class session that you miss.
| Week Number | Week starting | Lecture Topic | Prac Session | |
|---|---|---|---|---|
| 1 | 28 July | Neural Network models | no lab | |
| 2 | 4 August | Neural Network models | NN1 | |
| 3 | 11 August | Neural Network models | NN2 | |
| 4 | 18 August | Neural Network models | NN3 | |
| 5 | 25 August | Neural Network models | NN4 | |
| 6 | 1 September | Complex systems and evolutionary computation | NN5 | |
| 7 | 8 September | Complex systems and evolutionary computation | EC1 | |
| 8 | 15 September | Complex systems and evolutionary computation | EC2 | |
| 9 | 22 September | Complex systems and evolutionary computation | EC3 | |
| 29 September | ||||
| 10 | 6 October | Complex systems and evolutionary computation | CxSys1 | |
| 11 | 13 October | Symbolic models | CxSys2 | |
| 12 | 20 October | Embodied cognition |
No lab |
|
| 13 | 27 October | Review |
No lab |
|
| 3 November | Revision Period | |||
| Exam Week 1 | 10 November | |||
| Exam Week 2 | 17 November | |||
COGS2010 will be assessed by several methods as outlined below. Your final grade (on a 1 to 7 scale) will be determined by combining the marks from the various assessment components as described below. For each assessment item, reference is made to the specific learning objectives (from the list above) which the assessment item will address.
To pass the course, a student needs to pass both the oncourse and examination components.
Weekly labs are worth 40% total, the assignment is worth 10%.
Written answers to exercises are due at the end of each lab. Late penalty 1 mark per day unless given permission by the tutor. Lab exercises handed in later than 1 week will not be marked.
The assignment is due the last Thursday of semester (week 13).
The labs aid in the development of skills to construct, run, and interpret computational models using computer based simulation packages. They provide knowledge of specific models. The assignment allows individual development of a model, or exploration of one from the literature.
A two hour final examination will be held during the final examination period. This exam will be closed-book and will contain short-answer questions. Programmable calculators and other computing or communication devices are NOT permitted.
The exam may include any material from the course content, including lectures, assigned readings and lab exercises. The exam will comprise short answer questions and worked problems.
The exam will test the student's understanding of the basic facts and ideas that underpin computational approaches to cognition, the cognitive phenomena to which the models apply, and may also test the skills to simulate these architectures.
Assessment variation is possible for students with a disability (see HUPP 3.30.3 Special Arrangements for Examinations for Students with a Disability).
Laboratory Exercises: A student will have demonstrated a high level of performance in the lab exercises if in their submitted work they:-
Examination: A student will have demonstrated excellent knowledge in the exam if their performance:-
A grade of 7 will be awarded to a student who demonstrates excellent skills and knowledge as assessed by the examination, and who performs at a high level in the lab exercises and assignment.
A grade of 6 will be awarded to a student who demonstrates at least sound level of achievement/understanding in all assessment areas and achieves excellence in either the exam or the oncourse assessment.
A grade of 5 will be awarded to a student who demonstrates at least sound work in all assessment areas. Alternatively, a grade of 5 will be awarded to a student who demonstrates excellent knowledge/skill in one major area of assessment and basic knowledge/skills in the other areas.
A grade of 4 will be awarded to a student who demonstrates at least basic skills in all areas of assessment, or at least sound skills and sound knowledge levels in the majority of the assessed work.
Otherwise, a failing grade will be awarded
Written answers to exercises are due at the end of each lab. Late penalty 1 mark per day unless given permission by the tutor. Lab exercises handed in later than 1 week will not be marked.
Submission of the assignment will be announced in class. It will either be via the submission boxes on level one of the GPSouth building or via the online submission procedure. Check the course web page for details. Your assignment submission must be accompanied by a signed coversheet declaring that the submission is your original work. The assignment is due the last Thursday of semester (week 13). Late assignments will be penalised 20% per working day. Assignments received more than 5 days late will not be marked.
Lab reports and assignments will be available from the tutor.
The School and the wider academic community in general takes academic integrity and respect for other persons and property very seriously. In particular, the following behaviour is unacceptable:
Penalties for engaging in unacceptable behaviour can range from cash fines
or loss of grades in a subject, through to expulsion from the University.
You are required to read and understand the School Statement on Misconduct,
available on the ITEE website at:
http://www.itee.uq.edu.au/about/student-misconduct.jsp.
This Statement includes advice on how to properly cite references and other
sources in your submissions and on acceptable levels of collaboration.
If you have any questions concerning this statement, please contact your lecturer
in the first instance.
Timely feedback on all progressive assessment in this course will be available in accordance with University policy (HUPP 3.30.6 Student Access to Feedback on Assessment). The tutor is available for feedback during the stated office hours or at appropriate times during the lab.
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; Room 218, Building 1, Ipswich) regarding arrangements.
It is a student’s responsibility to incorporate feedback into their learning; making use of the assessment criteria that they are given; being aware of the rules, policies and other documents related to assessment; and providing teachers with feedback on their assessment practices.
Any student with a disability who may require alternative academic arrangements in the course is encouraged to seek advice at the commencement of the semester from a Disability Adviser at Student Support Services.