Course Details
Course Information Package
Course Unit Title | NEURAL NETWORKS AND FUZZY SYSTEMS | ||||||||
Course Unit Code | ACOE452 | ||||||||
Course Unit Details | |||||||||
Number of ECTS credits allocated | 5 | ||||||||
Learning Outcomes of the course unit | By the end of the course, the students should be able to:
| ||||||||
Mode of Delivery | Face-to-face | ||||||||
Prerequisites | NONE | Co-requisites | NONE | ||||||
Recommended optional program components | NONE | ||||||||
Course Contents | · Introduction to Neural Networks: Biological motivation; Classification and regression problems; Characteristics of Neural Networks; General structure of neurons. · Perceptrons: Computations of a perceptron; Representational interpretation and limitation; perceptron learning algorithm; Gradient descent; Delta learning algorithm. · Multilayer Neural Networks: Characteristics; The log-sigmoid, tan-sigmoid and softmax activation functions; The backpropagation training algorithm; Representational power; Issues: local minima and overfitting; Momentum term. · Self-Organizing Maps: Unsupervised learning; Competitive learning; Structure of SOM networks; Kohonen’s learning algorithm; Adding bias to learning. · Genetic Algorithms: Biological motivation; Search spaces; General structure of a genetic algorithm; Chromosome representation; Fitness function; Selection methods; Genetic operators; Convergence and diversity. · Applications of Genetic Algorithms: Combinatorial optimization with GAs; Solving pattern recognition problems with GAs; Hypothesis chromosome representation; Evolving neural network weights. | ||||||||
Recommended and/or required reading: | |||||||||
Textbooks |
| ||||||||
References |
| ||||||||
Planned learning activities and teaching methods | The course is delivered through three hours of lectures per week, which include presentation of new material and demonstration of concepts and algorithms. Lectures also include in-class exercises to enhance the material learning process and to assess the student level of understanding and provide feedback accordingly. Furthermore a lot of work is in done through homework and private study by carrying out the computations of the different techniques for specific inputs and by experimenting in Matlab with the application of these techniques to benchmark datasets. This provides students with practical experience on the ideas and issues discussed in class. All lecture notes and other material is available to students through the course homepage. | ||||||||
Assessment methods and criteria |
| ||||||||
Language of instruction | English | ||||||||
Work placement(s) | NO |