Soft Computing PG (7197.6)

This is an advanced unit in artificial intelligence emphasizing the Soft Computing discipline. Each semester, coverage is of two or three topics from Artificial Neural Networks, Fuzzy Systems, Genetic Algorithms, Software Agents, Artificial Life, and Complex Adaptive Systems. The unit content covers Soft Computing theory, fusion of two or more Soft Computing techniques and their applicability to real world applications. The choices depend on the availability of lecturers, student interest and research activity in the School. The unit has a strong practical focus with the implementation of Soft Computing techniques in laboratory exercises and assignments.
This unit may be cotaught with 7168 Soft Computing.

Learning outcomes

On successful completion of this unit, students will be able to:
1. Design and implement artificial neural networks for classification and regression tasks;
2. Synthesise and apply knowledge to solve problems involving uncertainty and imprecision using fuzzy logic;
3. Create genetic algorithms for solving optimization problems; and
4. Analyse and compare the performance of different soft computing techniques in realistic problems.

Graduate attributes

3. UC graduates are lifelong learners - reflect on their own practice, updating and adapting their knowledge and skills for continual professional and academic development
3. UC graduates are lifelong learners - evaluate and adopt new technology
2. UC graduates are global citizens - make creative use of technology in their learning and professional lives
1. UC graduates are professional - take pride in their professional and personal integrity
1. UC graduates are professional - work collaboratively as part of a team, negotiate, and resolve conflict
1. UC graduates are professional - display initiative and drive, and use their organisation skills to plan and manage their workload
1. UC graduates are professional - employ up-to-date and relevant knowledge and skills
3. UC graduates are lifelong learners - adapt to complexity, ambiguity and change by being flexible and keen to engage with new ideas

Prerequisites

None.

Corequisites

11512 Pattern Recognition and Machine Learning PG and 11521 Programming for Data Science G OR
8995 Software Technology 1 G

Incompatible units

7168 Soft Computing

Equivalent units

None.

Assumed knowledge

Basic mathematics.