1. To introduce students to a range of topics in the field of artificial neural networks, and to provide them with hands-on familiarity some of the established works.
2. To highlight some contemporary issues within the domain of neural computation with regard to biologically-motivated computing, particularly in relation to multidisciplinary research.
3. To emphasize the need to keep up-to-date in developing areas of science and technology and provide some skills necessary to achieve this.
4. To enable students to make reasoned decisions about the engineering of machine learning systems
A.Account for biological and historical developments of neural computation. Describe the nature and operation of Perceptron, MLP, Convolutional Neural Network (CNN), Competitive learning, Oja learning, and SOM networks, and when they are used assess the appropriate applications and limitations of ANNs;
B.Apply their knowledge to some emerging research issues in the field;
C.Understand how ANN models work in general terms and with respect to specific applications, e.g., regression, prediction and classification. The understanding will be reinforced by firsthand experiences in problem solving and assessed by course works and exam;
D.Understand some of the contemporary topics of artificial neural networks, and deep neural networks in particular, including CNN, with awareness of their advantages and applications,
E.Awareness of some of the modern machine learning concepts;
F.Familiarity with the essentials of Matlab and relevant toolboxes so as to enable exploration of the above in practical applications of ANNs.
1. Didactic component - the core of the teaching is lecture-based with Q/A and feedback. Lectures are supported by tutorials and labs / Practicals.
2. Self-learning component - students are encouraged to read around the subject materials.
3. Comprehension/review exercise - two continuous assessments, following supervised discussion and Q/A sessions in the seminars.
4. Case studies will be supplied to help students place the course material in context.