Introduction
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.
This course is credit-bearing (3 AU) and stackable to:
- Graduate Certificate in Signal Processing and Machine Learning (9 AU)
- FlexiMasters in Signal Processing and Machine Learning (15 AU)
- MSc in Signal Processing and Machine Learning (30 AU)
Learners will receive their Statement of Accomplishment (for a grade of D and above) or Certificate of Participation for this course—dependent upon their assessment performance.
This course is part of the FlexiMasters in Signal Processing and Machine Learning.
Note: Shortlisting will be conducted.
Course Availability
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Date(s): 10 Aug 2026 to 02 Jan 2027
Venue: Nanyang Technological University
Registration Closing Date: 13 Jul 2026
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Date(s): 10 Aug 2026 to 02 Jan 2027
Venue: Nanyang Technological University
Registration Closing Date: 13 Jul 2026
- 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.
Suitable for practicing engineers, hardware and software designers, data scientists, R & D managers, and industry planners who seek an understanding of current approaches and evolving directions for DSP and AI technologies. It is also intended for engineers and data scientists who anticipate future involvement in these areas.
As the micro-credential courses are only offered in this FlexiMasters in Signal Processing and Machine Learning programme, learners are required to enrol into this programme and complete all required courses within this programme.
Note: Shortlisting will be conducted.
Standard Course Fee: S$5615.68
| SSG Funding Support | BEFORE funding & GST | AFTER SSG funding (if eligible under various schemes) & 9% GST |
| Course Fee | Course Fee Payable | |
| Singapore Citizen (SC) and Permanent Resident (PR) (Up to 70% funding) | $5,152 | $1,684.70 |
| Enhanced Training Support for SMEs (ETSS) | $5,152 | $654.30 |
| Singapore Citizen aged ≥ 40 years old SkillsFuture Mid-career Enhanced Subsidy (MCES) (Up to 90% funding) | $5,152 | $654.30 |
- NTU/NIE alumni may utilise their $1,600 Alumni Course Credits for this course. Click here for more information.
- Learners can utilise their SkillsFuture Credits for this course.
- Singaporeans aged 40 years and above are able to use their SkillsFuture Credit (Mid-Career) top-up of $4,000 to offset the course fee after SSG funding.
- Real-time DSP Design & Applications (3AU)
- Natural Language Processing (3 AU)
- Analytic & Ensemble Machine Learning (3 AU)
- Video Analysis and Processing (3 AU)
