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Computational Intelligence, Introduction
Computing professionals are required to solve increasingly complex problems using new algorithms, systems or programming paradigms. Fortunately, "black-box" computational intelligence tools can be configured to allow their application to problems, without the user's intimate knowledge of the low-level details. This course is well-suited to computational professionals who are interested in exploring new techniques for solving problems that are ill-defined, have conflicting constraints, or contain data with high noise levels. Industrial applications can be found in software algorithm development, electronic design automation, data mining, medical diagnosis, and pattern matching, etc.
You will learn the strengths and weaknesses of various computational and artificial intelligence (AI) techniques using supplied software.
The course introduces the following topics:
- Search spaces and their importance for assessing problem complexity
- Evolutionary computation, the fundamental engine behind many AI techniques
- Genetic programming with many examples
- Neural networks and the iris problem
- Swarm intelligence, the power of collective, decentralized systems
- Support vector machines demonstration using a popular tool for simple classification
- Fuzzy logic, including a solution of the traveling salesman problem
You will learn to solve AI problems using software on a provided bootable USB Stick and learn techniques for representing and structuring real-world problems using AI. By the end of the course, you will understand common algorithms and techniques used to solve real-world optimization problems, and also gain experience applying them to practical problems.
Learning Outcomes:At the conclusion of the course, you should be able to:
- Determine if a particular task is suitable for a computational intelligence technique
- Apply the appropriate methods to the problem of interest.
Skills Needed: Experience with a computer programming language and basic algebra skills.
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