Requisites: Pre-requisite: Algorithms and Data Structures based knowledge within a high level programming language.

Computational intelligence (CI) is a field of artificial intelligence that focuses on developing algorithms, models, and systems that can mimic biological or naturally occurring systems, aiming to solve complex real-world problems. CI emphasizes techniques that deal with uncertainty, approximation, and learning from experience.The key components covered in the module include:1. Neural Networks: These are inspired by the human brain's structure and function. They consist of interconnected layers of nodes (neurons) that can learn patterns from data and improve over time.2. Fuzzy Logic: A form of logic that allows for reasoning with vague or imprecise data, often used in situations where there’s uncertainty or gradation in truth values (e.g. for speed we could have, slow, average, fast).3. Evolutionary Algorithms: These algorithms mimic the process of natural evolution and Darwinian survival of the fittest, using techniques like selection, mutation, and crossover to gradually evolve better solutions. Examples include genetic algorithms and genetic programming.Computational intelligence techniques can be applied to areas such as data mining, optimization, robotics, natural language processing, and machine learning, where traditional approaches may fall short due to their complexity or data requirements

Upon completion of this module you will be able to

LO1: Critically evaluate the use of soft computational intelligence techniques.

LO2: Compare and contrast the use of differing techniques to achieve particular functionalities.

LO3: Specify, implement and customise a computational intelligence algorithm.

LO4: Apply appropriate evaluation techniques in order to analyse an algorithm’s effectiveness.