This unit looks at the development and application of biologically inspired models of computation. We study: basic components of a natural neural systems: synapses, dendrites and neurons and their computational models; fundamental concepts of data and signal encoding and processing; neural network architectures: pattern association networks, auto associative networks, feedforward networks, competitive networks, self organizing networks and recurrent networks; plasticity and learning. Hebb rule, supervised learning, reinforced learning, error-correcting learning, unsupervised learning, competitive learning, self-organization.
Minimum total expected workload equals 12 hours per week comprising:
(a.) Contact hours for on-campus students:
(b.) Additional requirements (all students):
See also Unit timetable information
CSE5301
Grace Rumantir
Consultation hours: TBA
Monash is committed to excellence in education and regularly seeks feedback from students, employers and staff. One of the key formal ways students have to provide feedback is through the Student Evaluation of Teaching and Units (SETU) survey. The University’s student evaluation policy requires that every unit is evaluated each year. Students are strongly encouraged to complete the surveys. The feedback is anonymous and provides the Faculty with evidence of aspects that students are satisfied and areas for improvement.
For more information on Monash’s educational strategy, see:
www.monash.edu.au/about/monash-directions/ and on student evaluations, see: www.policy.monash.edu/policy-bank/academic/education/quality/student-evaluation-policy.html
This unit was offered for the first time in Semester 1, 2010. It has consistently been getting well above 4 out of 5 on every question in the unit evaluation with close to 100% response rate every year. We will try to continue maintaining the high quality of the unit this year. Most notable characteristics of the unit as pointed out by previous student feedback are:
If you wish to view how previous students rated this unit, please go to
https://emuapps.monash.edu.au/unitevaluations/index.jsp
| Week | Activities | Assessment |
|---|---|---|
| 0 | No formal assessment or activities are undertaken in week 0 | |
| 1 | Introduction | There is a self-assessed test (not marked) on basic maths and statistics on Moodle that will be discussed in the Week 1 tutorial. Please complete this to see if you need to do further study prior to completing this unit. |
| 2 | Artificial Neural Networks: an Overview | |
| 3 | Perceptron for Linear Pattern Classification | |
| 4 | Neural Networks for Non-linear Pattern Recognition 1 | |
| 5 | Neural Networks for Non-linear Pattern Recognition 2 | |
| 6 | Generalisation and Improving Neural Networks Performance | |
| 7 | Unsupervised Classification with Self Organising Maps | |
| 8 | Unit Test (in lecture time slot, tutorials still on) | Unit Test during Week 8 lecture (Monday 27 April 2015) |
| 9 | Associative Memory Networks | Assignment Stage 1 during Week 9 tutorial |
| 10 | Neural Networks for Time series Forecasting | |
| 11 | Recurrent Networks for Time series Forecasting | Assignment Stage 2 due start of Week 11 lecture (Monday 18 May 2015) |
| 12 | Revision | |
| SWOT VAC | No formal assessment is undertaken in SWOT VAC | |
| Examination period | LINK to Assessment Policy: http://policy.monash.edu.au/policy-bank/ academic/education/assessment/ assessment-in-coursework-policy.html |
*Unit Schedule details will be maintained and communicated to you via your learning system.
Examination (3 hours): 60%; In-semester assessment: 40%
| Assessment Task | Value | Due Date |
|---|---|---|
| Unit Test | 20% | Unit Test during Week 8 lecture (Monday 27 April 2015) |
| Applications of Neural Network Algorithms | 20% | Assignment Stage 1 during Week 9 tutorial, Assignment Stage 2 due start of Week 11 lecture (Monday 18 May 2015) |
| Examination 1 | 60 % | To be advised |
Faculty Policy - Unit Assessment Hurdles (http://intranet.monash.edu.au/infotech/resources/staff/edgov/policies/assessment-examinations/assessment-hurdles.html)
Academic Integrity - Please see resources and tutorials at http://www.monash.edu/library/skills/resources/tutorials/academic-integrity/
Correct answers to questions, and quality of solutions to problems, which demonstrates understanding of the learning materials.
Further detail of the format and coverage of the unit test will be made available on Moodle.
The assignment will be in paired groups.
Stage 1: Write up of problem definition, data analysis and pre-processing, and design of experiments (non assessable).
Stage 2: Submission (20%).
Students will be assessed on:
The tutor will monitor individual contributions when allocating marks to members of the group.
Further assessment criteria and marking sheet will be made available on the unit Moodle site.
Monash Library Unit Reading List (if applicable to the unit)
http://readinglists.lib.monash.edu/index.html
Types of feedback you can expect to receive in this unit are:
Submission must be made by the due date otherwise penalties will be enforced.
You must negotiate any extensions formally with your campus unit leader via the in-semester special consideration process: http://www.monash.edu.au/exams/special-consideration.html
It is a University requirement (http://www.policy.monash.edu/policy-bank/academic/education/conduct/student-academic-integrity-managing-plagiarism-collusion-procedures.html) for students to submit an assignment coversheet for each assessment item. Faculty Assignment coversheets can be found at http://www.infotech.monash.edu.au/resources/student/forms/. Please check with your Lecturer on the submission method for your assignment coversheet (e.g. attach a file to the online assignment submission, hand-in a hard copy, or use an electronic submission). Please note that it is your responsibility to retain copies of your assessments.
If Electronic Submission has been approved for your unit, please submit your work via the learning system for this unit, which you can access via links in the my.monash portal.
Please check with your lecturer before purchasing any Required Resources. Limited copies of prescribed texts are available for you to borrow in the library, and prescribed software is available in student labs.
You will need access to a Neural Network tool such as:
All the above softwares are available in the 24 hour labs B3.45, B3.46, B3.46b at the Caulfield Campus. Submit an online IT request to gain access to these labs at http://www1.infotech.monash.edu.au/webservices/servicedesk/requestform/
Scientific Calculator
Monash has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University’s academic standards, and to provide advice on how they might uphold them. You can find Monash’s Education Policies at: www.policy.monash.edu.au/policy-bank/academic/education/index.html
Important student resources including Faculty policies are located at http://intranet.monash.edu.au/infotech/resources/students/
The University provides many different kinds of support services for you. Contact your tutor if you need advice and see the range of services available at http://www.monash.edu.au/students. For Malaysia see http://www.monash.edu.my/Student-services, and for South Africa see http://www.monash.ac.za/current/.
The Monash University Library provides a range of services, resources and programs that enable you to save time and be more effective in your learning and research. Go to www.lib.monash.edu.au or the library tab in my.monash portal for more information. At Malaysia, visit the Library and Learning Commons at http://www.lib.monash.edu.my/. At South Africa visit http://www.lib.monash.ac.za/.