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Course Details
Course Details
What You'll Learn
Topic 1: Overview of Computer Vision with Deep Learning
Introduction to Computer Vision
Computer Vision Business Applications
Classical vs Deep Learning Computer Vision
Topic 2: Basic Computer Vision Techniques
Setup Portable Mini Computer Board
Setup Camera
Setup OpenCV
Image Processing with OpenCV
Topic 3: Image Classification
Introduction to Deep Learning and Neural Network
Overview of Convolutional Neural Network (CNN)
Pre-Trained Vision Models
Image Classification with Pre-Trained CNN
Topic 4: Object Detection
Overview of Single Shot Detector (SSD) Object Detection Technique
Object Detection and Video Tracking with SSD
Overview of YOLO Object Detection Technique
Object Detection and Video Tracking using YOLO
Basic Video Analytics
Topic 5: Computer Vision Systems
Edge Based Vision System
Cloud Based Vision System
Assessment
- Written Exam
- Practical Exam
Course Info
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
Minimum Software/Hardware Requirement
Software:
You will need the following for this course:
- Python 3 – free
- Visual Studio Code – free
- Google Colab – free, runs in the browser with a Google account
Hardware: Windows PC or Mac laptop
Funding Eligibility Criteria
Self-sponsored learners (individuals)
- Singapore Citizens and Singapore Permanent Residents aged 21 and above.
- Up to 50% course fee funding. Singapore Citizens aged 40 and above receive up to 70% under the Mid-career Enhanced Subsidy (MCES).
Company-sponsored learners
- Singapore Citizens and Singapore Permanent Residents who are direct employees of the sponsoring company.
- Up to 50% course fee funding, or up to 70% for employees of SMEs (Enhanced Training Support for SMEs) and for Singapore Citizens aged 40 and above. An SME has no more than 200 employees or an annual turnover of no more than $100 million.
- Employers can also claim Absentee Payroll funding for employees who attend during working hours.
- Eligible companies can offset their out-of-pocket fees with SkillsFuture Enterprise Credit (SFEC).
Conditions for all funded learners
- Attendance is taken digitally with Singpass for every session, including the assessment.
- You must complete the course and pass all assessments (assessed as Competent).
- If these conditions are not met, the funding is clawed back and the learner or employer pays the full course fee.
- 9% GST is charged on the full course fee before funding.
- Promo and discount codes cannot be used on funded courses.
- Any balance after funding can be paid in cash, with PSEA (Singapore Citizens with a PSEA), or partly reimbursed by UTAP for NTUC members.
How to Apply SkillsFuture Enterprise Credit (SFEC)
SFEC helps companies pay for employee training. The current SFEC ends on 30 November 2026, and a redesigned SFEC starts on 1 December 2026.
Current SFEC: classes ending on or before 30 November 2026
- Eligible companies received a one-off $10,000 credit, notified by letter to their Corppass administrator. Eligibility generally required at least 3 Singapore Citizen or PR employees and Skills Development Levy contributions.
- SFEC covers up to 90% of the company's out-of-pocket course fee after other subsidies.
- Check your company's SFEC eligibility and balance on EnterpriseSG.
- Register your Singapore Citizen or PR employee for a class that ends by 30 November 2026, as company-sponsored, and pay the course fee after funding.
- Your employee attends and completes the course. We submit the attendance and assessment results to SWDA.
- No separate claim is needed. SFEC is calculated automatically once the course fee grant is approved and is paid quarterly to your company's IRAS GIRO or PayNow Corporate account.
Any unused balance of the current SFEC expires on 30 November 2026.
Redesigned SFEC from 1 December 2026
Companies with at least 3 resident employees receive a fresh $10,000 in an online SFEC wallet that offsets out-of-pocket fees upfront at enrolment, instead of reimbursing them afterwards. SWDA has not yet published the support rates and claim steps; see OurWorkforce or our SFEC guide.
How to Apply UTAP (NTUC Members)
NTUC members can claim 50% of the unfunded course fee under the Union Training Assistance Programme (UTAP), capped at $250 a year (aged below 40) or $500 a year (aged 40 and above). The cap is shared with UTAP claims for AI tool subscriptions.
Eligibility: you must be an NTUC member paying full membership fees, with your membership paid up before and during the course and when you claim. The course must not be fully paid for by your employer or another source.
- Make sure your NTUC membership is paid up before the course starts.
- Register for this course with us and pay the course fee after funding.
- Attend at least 75% of the course and sit all assessments.
- Within 6 months after the course ends, go to the NTUC U Portal, open UTAP under E-services, log in with Singpass and choose Course Fee Claim.
- Enter the nett course fee you paid (after all subsidies and SkillsFuture Credit, including GST), and upload your invoice or receipt and your certificate or result slip (PDF, JPG or PNG, up to 5MB each).
- NTUC usually pays the claim within 4 weeks of submission.
Read more in our UTAP guide.
How to Apply PSEA
Singapore Citizens with a Post-Secondary Education Account (PSEA), which stays open until around the year you turn 31, can use it to pay the course fee after funding for PSEA-approved courses. You can also use a sibling's PSEA after your own has been used up.
- Check that this course shows the PSEA tag on its MySkillsFuture page.
- Check your PSEA balance on MOE's Student Finance System with Singpass.
- Download and complete the PSEA withdrawal form, then submit it to us.
- We submit the withdrawal request to MOE. You (and your sibling, if you are using their account) authorise it in the Student Finance System. Nothing is withdrawn without this authorisation.
- MOE pays us directly. If you have already paid, we refund the amount covered by PSEA.
More details: MOE PSEA overview.
Appeal Process
If you disagree with an assessment decision, you have the right to appeal.
- When the assessor gives you feedback, say whether you agree with the outcome. If you agree, you and the assessor sign the Assessment Summary Record; if you disagree, do not sign it.
- Discuss the decision with the assessor or the assessment manager first.
- If you are still not satisfied, tell the assessor you intend to appeal. The assessor records this in the feedback section of the Assessment Summary Record and informs the assessment manager.
- Lodge your appeal within 7 days, giving your reasons, using the Candidate Appeal Form. The assessor can help you write and lodge it.
- An Assessment Appeal Panel reviews the information from you and the assessor and makes a decision. A record of the appeal and its outcome is kept.
- You will be told the outcome within 2 weeks of lodging the appeal. The panel's decision is final.
Job Roles
Job Roles
- Data Scientist
- Machine Learning Engineer
- AI Researcher
- Robotics Engineer
- Computer Vision Engineer
- Image Processing Specialist
- Software Developer (with a vision focus)
- Biometrics Specialist
- AR/VR Developer
- Video Analytics Developer
- Autonomous Systems Engineer
- Smart Camera Developer
- Medical Imaging Specialist
- Surveillance System Designer
- Visual Effects (VFX) Artist
Trainers
Trainers
Dr. Alfred Ang combines formal training in intelligent robotics with years of hands-on work on cameras, boards and on-device vision. He completed the Graduate Diploma in Intelligent Robotics Systems at NUS-ISS in 2021, holds a PhD in Electrical Engineering from NUS, an MSc in Electrical and Electronic Engineering from NTU and a B.Sc (Hons) in Physics from NUS, and has worked in the defence, electronics and semiconductor industries. In 2015 he founded Tertiary Robotics, which offered Arduino, Raspberry Pi, Microbit and robotics products, and his interests include machine learning, deep learning, artificial intelligence, the Internet of Things and robotics. He is the founder of Tertiary Courses and serves as Managing Director, CIO, CTO and Chief Instructional Designer of Tertiary Infotech Academy Pte Ltd, and he is an IBM certified instructor for the AI Practitioners course, a Microsoft Certified AI Engineer Associate (AI-102) and Data Scientist (DP-100), and an AWS Certified Solutions Architect and AI Practitioner. Several of his public GitHub projects apply computer vision in shipped apps: scannerapp-android uses ML Kit edge detection and on-device OCR to scan documents, passportphotoapp performs on-device background removal and face-aware cropping, and tapcardapp scans business cards with live edge detection and Vision OCR. iotplatform, a self-hosted IoT platform connecting Arduino, ESP32 and Raspberry Pi devices over MQTT and HTTP, reflects the edge-to-cloud architecture this course closes with. He has published more than 20 peer-reviewed papers and co-invented more than 20 US patents.
In this CASL Computer Vision for Beginners course, Dr. Ang takes learners from first principles to working object detection on real hardware. The first topic gives an overview of computer vision with deep learning, its business applications and the difference between classical and deep learning approaches. In the second topic learners set up a portable mini computer board and camera, install OpenCV and practise image processing techniques such as resizing, filtering, thresholding and edge detection, an area where his robotics and electronics background helps when hardware does not behave. The third topic introduces deep learning and neural networks, explains convolutional neural networks and has learners classify images with pre-trained CNN models. The fourth topic covers object detection with Single Shot Detector (SSD) and YOLO, including object detection and tracking in video and basic video analytics. The final topic compares edge-based and cloud-based vision systems, weighing latency, cost, privacy and bandwidth, which draws on his own experience building on-device vision features in mobile apps and connecting devices to cloud services through his IoT platform. His teaching is hands-on and beginner-friendly: learners write and run code at every stage, see results on a live camera feed and leave understanding how a complete vision system is put together, from capture to decision. He explains the mathematics only as far as it builds intuition, which keeps the course accessible to beginners. As an ACTA-certified trainer and DACE-certified curriculum developer, he aligns each activity with the CASL assessment requirements. To see his vision, IoT and robotics projects, view his GitHub portfolio at https://github.com/alfredang and connect with him on LinkedIn at https://www.linkedin.com/in/angchewhoe/
Richard Wan is an ACLP-certified lecturer with over 40 years of experience in software and hardware development, specialising in computer vision, AI, machine learning and embedded systems. A government scholar, he holds a Master's in Computer Vision and a Bachelor's in Electrical Engineering and Computer Science from the University of Wisconsin, Madison. He co-founded several high-tech companies, including SGX-listed Mediaring Ltd and E-Book Systems, has extensive R&D experience at Singapore's National Computer Board, and has taught Python, machine learning, deep learning and computer vision frameworks such as OpenCV, TensorFlow and PyTorch.
In CASL Computer Vision for Beginners, Richard emphasises practical applications so learners understand both the theory and the implementation of computer vision systems. Participants explore computer vision with deep learning and its business applications, set up a portable mini computer board, camera and OpenCV for image processing, and then move into image classification with pre-trained convolutional neural networks and on to object detection. Drawing on his experience across healthcare, manufacturing and defence, he equips participants with industry-relevant skills to design and deploy beginner-level computer vision applications of their own.
Tan Woei Ming is an ACLP-certified trainer and data science professional whose career has centred on semiconductor and manufacturing analytics. At Micron Semiconductor, he developed IoT-enabled cloud solutions, wafer map pattern recognition models and deep learning algorithms for scratch detection and root cause analysis, and he has led AI initiatives in predictive analytics, automation and process optimisation. He holds a Master's in Intelligent Systems from NUS and a First-Class Honours degree in Electrical and Electronic Engineering from NTU, and focuses on translating complex AI technologies into practical business applications.
CASL - Computer Vision for Beginners draws directly on this pattern recognition and deep learning work. Woei Ming introduces computer vision and its business applications, compares classical and deep learning approaches, and has learners set up a portable mini computer board, camera and OpenCV for image processing. Participants then classify images with convolutional neural networks and pre-trained models, detect and track objects in video with SSD and YOLO, and compare edge-based and cloud-based vision systems. By integrating real industry projects with hands-on implementation, he ensures learners gain both conceptual clarity and practical skills to solve problems with computer vision.
Shawn Koh is an ACTA-certified trainer, entrepreneur and IoT and AI solutions expert with more than 25 years of experience in software development, project management and system integration. As IoT Project Director at I.O.T. Workz Pte. Ltd., he has delivered smart office, smart home and IoT lab solutions for schools and enterprises, and he has been invited as a Smart Nation guest speaker on IoT and AI. He holds a Master's in Technopreneurship and Innovation from NTU and a Bachelor's in Computer Science from SIM, alongside certifications in PMP, Linux Administration and Information Security.
In CASL Computer Vision for Beginners, Shawn emphasises practical, beginner-friendly applications such as image classification, object detection and integration with IoT systems. Learners start with an overview of computer vision with deep learning and its business applications, then set up a portable mini computer board and camera, process images with OpenCV, and move on to neural networks, convolutional neural networks and pre-trained vision models for image classification. His approach blends coding exercises with real-world examples, so participants understand how computer vision is applied in automation, smart systems and everyday technologies, and leave confident to explore the fundamentals of computer vision on their own.
Review
Customer Reviews (41)
- Recommended Review by Course Participant/Trainee
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Radio-4 not functioning well (Posted on 11/21/2025)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - Recommended Review by Course Participant/Trainee
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Nil (Posted on 11/21/2025)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - . Review by Course Participant/Trainee
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The trainer made a commendable effort to deliver the material despite several challenges. However, the overall course structure and technical setup could be improved. The program relied heavily on specific code that appeared insufficiently tested, which led to multiple technical issues during the session. Unstable Wi-Fi added further complications, and the dependency on Raspberry Pi devices made troubleshooting time-consuming, especially when installations had to start from scratch. To enhance future sessions, it would be helpful for the school to provide better preparation and contingency plans—such as pre-installed, fully functional Raspberry Pi units or backup systems—so participants can focus on learning rather than resolving technical hurdles. Ensuring that all course materials and code are thoroughly tested beforehand would also make the training smoother and more effective. The topic itself is valuable and relevant, and with improved planning and technical readiness, this course has the potential to deliver a much better learning experience. (Posted on 11/21/2025)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - Recommended Review by Course Participant/Trainee
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. (Posted on 11/21/2025)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - Recommended Review by Course Participant/Trainee
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. (Posted on 11/21/2025)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment
