Introduction
This course offers a foundational overview of machine learning, examining both analytic and ensemble approaches. Beginning with basic concepts, the course then progresses to linear regression, classification, kernel-based techniques. Learners will then explore ensemble approaches like bagging and boosting, including classic algorithms like Random Forest, AdaBoost, and traditional Gradient Boosting before advancing to the more popular eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). The curriculum also introduces reinforcement learning and its relationship to ensemble methods, with practical industry examples. Learners can enhance employability and position themselves for roles in Artificial Intelligence (AI) and machine learning engineering upon the completion of this course.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.
At the end of the course, learners will be able to:
- Implement and use data preprocessing, linear models and learning score functions.
- Implement and use analytic regression, classification and kernel methods.
- Implement bagging framework and random forest algorithms to solve classification and regression problems.
- Describe and evaluate the boosting framework, various boosting algorithms, and reinforcement learning.
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 (3 AU)
- Natural Language Processing (3 AU)
- Genetic Algorithms & Machine Learning (3 AU)
- Video Analysis and Processing (3 AU)
