Genetic Algorithms & Machine Learning
About This Course
This course approaches the tackling of complex and real-world problems with genetic algorithms and machine learning. Learners will explore optimisation techniques using genetic algorithms for solving challenges with mixed real-integer variables, numerous locally optimal solutions, and discontinuities; and traverse key machine learning concepts, theories and algorithms, for analysing supervised and unsupervised learning algorithms. This micro-credential enables learners to leverage machine learning methods for data analytics and pattern recognition. By the end of the course, learners would be able to apply various evolutionary optimization algorithms to solve problems in their own research fields.
As this micro-credential course is only offered in the FlexiMasters in Signal Processing and Machine Learning programme, learners are required to enrol into the programme and complete all required courses within this programme.
What You'll Learn
• Explain the theories of evolutionary algorithms.
• Apply evolutionary algorithms to formulate and solve various optimisation problems
• Analyse supervised learning and unsupervised learning algorithms, and compare various classifiers and clustering algorithms
• Design and implement solutions for real-world problems using appropriate machine learning methods.
Entry Requirements
No pre-requisites are needed to enroll in the individual courses; however, learners without qualifications or working experience in relevant engineering or the related fields may find the course contents challenging.
Note: Shortlisting will be conducted.