WSQ - Pattern Recognition and Machine Learning with R

Topic 1: Overview of Machine Learning

• Introduction to Machine Learning
• Pattern Recognition Problems Suitable for Machine Learning
• Supervised vs Unsupervised Learnings
• Types of Machine Learning
• Machine Learning Techniques
• R Packages for Machine Learning

Topic 2: Regression

• What is Regression
• Applications of Regression
• Least Square Error Minimization
• Data Pre-processing
• Bias vs Variance Trade-off
• Regression Methods with Regularization
• Logistic Regression

Topic 3: Classification

• What is Classification
• Applications of Classification
• Classification Algorithms
• Confusion Matrix
• Classification Performance Evaluation

Topic 4: Clustering

• What is Clustering
• Applications of Clustering
• Distance Measure
• Clustering Algorithms
• Clustering Performance Evaluation
• Anomaly Detection Problem

Topic 5: Principal Component Analysis

• • Principal Component Analysis (PCA) and Dimension Reduction
• • Applications of PCA
• • PCA Workflow

Topic 6: Deep Learning

• What is Neural Network
• Activation Functions
• Loss Function Minimization
• Gradient Descent Algorithms and Learning Rate
• Deep Neural Network for Visual Recognition
• Improve Visual Recognition with Convolutional Neural Network
• The Future of AI
• AI Ethics

Final Assessment

• Written Assessment - Short Answer Questions (WA-SAQ)
• Practical Performance (PP)

Promotion Code

Promo or discount cannot be applied to WSQ courses

Minimum Entry Requirement

Knowledge and Skills

• Able to operate using computer functions with minimum Computer Literacy Level 2 based on ICAS Computer Skills Assessment Framework
• Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)

Attitude

• Positive Learning Attitude
• Enthusiastic Learner

Experience

• Minimum of 1 year of working experience.
• Minimum 18 years old

Software Requirement

Please download and install the following software prior to the class

• R
  
• R Studio
  

About Progressive Wage Model (PWM)

The Progressive Wage Model (PWM) helps to increase wages of workers through upgrading skills and improving productivity. 

Employers must ensure that their Singapore citizen and PR workers meet the PWM training requirements of attaining at least 1 Workforce Skills Qualification (WSQ) Statement of Attainment, out of the list of approved WSQ training modules.

For more information on PWM, please visit MOM site.

Funding Eligility Criteria

Steps to Apply Skills Future Claim

• The staff will send you an invoice with the fee breakdown.
• Login to the MySkillsFuture portal, select the course you’re enrolling on and enter the course date and schedule.
• Enter the course fee payable by you (including GST) and enter the amount of credit to claim.
• Upload your invoice and click ‘Submit’

SkillsFuture Level-Up Program

The  SkillsFuture Level-Up Programme provides greater structural support for mid-career Singaporeans aged 40 years and above to pursue a substantive skills reboot and stay relevant in a changing economy. For more information, visit SkillsFuture Level-Up Programme

Get Additional Course Fee Support Up to $500 under UTAP

The Union Training Assistance Programme (UTAP) is a training benefit provided to NTUC Union Members with an objective of encouraging them to upgrade with skills training. It is provided to minimize the training cost. If you are a NTUC Union Member then you can get 50% funding (capped at $500 per year) under Union Training Assistance Programme (UTAP).

For more information visit NTUC U Portal – Union Training Assistance Program (UTAP)

Steps to Apply UTAP

• Log in to your U Portal account to submit your UTAP application upon completion of the course.

Note

• SSG subsidy is available for Singapore Citizens, Permanent Residents, and Corporates.
• All Singaporeans aged 25 and above can use their SkillsFuture Credit to pay. For more details, visit www.skillsfuture.gov.sg/credit
• An unfunded course fee can be claimed via SkillsFuture Credit or paid in cash.
• UTAP funding for NTUC Union Members is capped at $250 for 39 years and below and at $500 for 40 years and above.
• UTAP support amount will be paid to training provider first and claimed after end of class by learner.

Appeal Process

1. The candidate has the right to disagree with the assessment decision made by the assessor.
2. When giving feedback to the candidate, the assessor must check with the candidate if he agrees with the assessment outcome.
3. If the candidate agrees with the assessment outcome, the assessor & the candidate must sign the Assessment Summary Record.
4. If the candidate disagrees with the assessment outcome, he/she should not sign in the Assessment Summary Record.
5. If the candidate intends to appeal the decision, he/she should first discuss the matter with the assessor/assessment manager.
6. If the candidate is still not satisfied with the decision, the candidate must notify the assessor of the decision to appeal. The assessor will reflect the candidate’s intention in the Feedback Section of the Assessment Summary Record.
7. The assessor will notify the assessor manager about the candidate’s intention to lodge an appeal.
8. The candidate must lodge the appeal within 7 days, giving reasons for appeal 
9. The assessor can help the candidate with writing and lodging the appeal.
10. he assessment manager will collect information from the candidate & assessor and give a final decision.
11. A record of the appeal and any subsequent actions and findings will be made.
12. An Assessment Appeal Panel will be formed to review and give a decision.
13. The outcome of the appeal will be made known to the candidate within 2 weeks from the date the appeal was lodged.
14. The decision of the Assessment Appeal Panel is final and no further appeal will be entertained.
15. Please click the link below to fill up the Candidates Appeal Form.

• Data Scientist
• Machine Learning Engineer
• Statistician
• R Developer
• Quantitative Researcher
• Bioinformatics Scientist
• Predictive Modeler
• Data Analyst (focusing on advanced analytics)
• Big Data Specialist (using R)
• Algorithm Developer
• AI Researcher (using R)
• Financial Quantitative Analyst
• Business Intelligence Specialist (with R expertise)
• Marketing Analytics Specialist (using R)
• Epidemiologist (utilizing machine learning).

Dwight Nuwan Fonseka – Dwight Nuwan Fonseka is an ACLP-certified trainer and Head of Data Science at Plano Pte. Ltd., where he leads projects in predictive modeling, deep learning, and healthcare analytics. He is also an adjunct lecturer at the London School of Business and Finance (LSBF), coordinating the Diploma in Data Analytics, and an associate trainer with Tertiary Courses. His expertise spans R programming, R Shiny applications, and machine learning frameworks such as h2oAI and Keras, which he applies to real-world problems in text mining, time series analysis, and image analytics.

Dwight’s training emphasizes practical, project-based learning in R for pattern recognition and machine learning. He equips learners with skills to perform data preparation, apply supervised and unsupervised learning techniques, and build predictive models using R libraries. With his extensive teaching and industry background, Dwight ensures participants not only understand the theoretical underpinnings of pattern recognition but also gain the confidence to implement models effectively in business and research contexts.

Dr Alvin Ang – Dr Alvin Ang is an ACLP-certified trainer with a Ph.D. in Operations Research from Nanyang Technological University and more than a decade of academic and industry experience. He has taught at NTU, SUSS, Curtin University, and SP Jain School of Global Management, as well as serving as an IBM Data Science Instructor. His professional expertise spans machine learning, optimization, and quantitative methods, complemented by multiple IBM certifications in Python, R, TensorFlow, and advanced analytics. He is also the founder of DataFrens.sg, an open-source data science community that promotes applied machine learning in Singapore.

In his training, Dr Ang focuses on guiding learners through R-based machine learning workflows, including classification, clustering, and dimensionality reduction. He emphasizes the practical application of pattern recognition techniques to real-world datasets, ensuring learners gain both technical depth and applied problem-solving skills. By blending theory with hands-on coding exercises, Dr Ang equips participants with the knowledge to design and deploy machine learning models for research and business innovation.

Khoo Yong – Khoo Yong is an ACTA-certified trainerwith strong expertise in data analytics, statistics, and applied programming. With years of experience in adult training, he specializes in simplifying complex technical concepts for learners from diverse backgrounds. His expertise includes teaching R programming and statistical modeling, where he emphasizes both conceptual understanding and practical application.

In his R training, Khoo guides learners through the foundations of pattern recognition and machine learning, including regression, clustering, and model evaluation. His teaching approach is highly interactive and practice-driven, ensuring participants gain the confidence to explore data, build models, and interpret outcomes. By combining his adult education background with technical proficiency, Khoo empowers learners to apply machine learning fundamentals effectively in professional and academic contexts.