Vision Systems

Course Content

• Computer vision fundamentals, OpenCV.
• Hand-crafted, supervised, and self-supervised feature representation learning (e.g., SimCLR, MoCo).
• Deep learning foundation for vision systems (e.g., CNN, Attention).
• Deep learning applications including classification (e.g., ResNet), detection (e.g., YOLO, CenterNet), and segmentation (e.g., UNet, Mask R-CNN).
• Practical case studies, workshops, and minimum viable products for vision systems.

• Identifying specific vision system requirements tailored to diverse industrial applications.
• A solid grasp of the foundational principles of computer vision technology, including essential theories and algorithms for vision analytics.
• Competence in designing and implementing computer vision algorithms to address real-world industrial challenges.
• Skills to design and construct advanced vision systems for a range of sectors, including security surveillance, manufacturing, consumer electronics, healthcare, and urban solutions.

Who Should Attend

• Data Scientists seeking to deepen their domain knowledge in vision systems, enriching their data analytics capabilities with valuable insights specific to vision applications.
• Engineers tasked with the conception, development, implementation, and evaluation of software and hardware solutions within the scope of vision systems across various industries.
• Product managers overseeing projects and products that incorporate vision systems, aiming to ensure successful delivery and performance.
• Working professionals in related fields who wish to update their skill set or reinforce their existing competencies in vision systems; to stay abreast of technological advancements.

Prerequisites

• Participants should have intermediate skills in Python programming (e.g., Numpy, Pandas), and/or OpenCV programming (e.g., able to apply filtering and transformation on the image).
• Experienced in using Jupyter Notebooks, Google Colab, and well-versed in package installation.

Course Logistics

• No Printed Materials: Course materials are accessed digitally. Do kindly note that no printed copies of course materials will be issued.
• Device Requirements: Bring an internet-enabled device (laptop, tablet, etc) with power chargers to access and download course materials.

Fees and Subsidies

Note:
1. SSG Funding is available to qualified individuals, subject to meeting the attendance requirement and passing of assessment.
2. All self-sponsored Singaporeans aged 25 and above can use their SkillsFuture Credit to pay for course fees. For more information about SkillsFuture Credit, click here.
3. SME fees are applicable only to participants who are sponsored by small and medium enterprises.
4. SSG Funding is valid up to 30 September 2027.

Certificate

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NUS-ISS Course Registration Terms and Conditions

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NUS-ISS and Learner’s Commitment and Responsibilities

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WIFI Access

WIFI access will be made available to participants.

Venue

NUS-ISS
25 Heng Mui Keng Terrace
Singapore 119615

Click HERE for directions to NUS-ISS

In the event of a change of venue, participants are advised to refer to the acceptance email sent one week prior to the commencement date.

Course Confirmation

All classes are subject to confirmation and NUS-ISS will send an acceptance email to participants one week prior to the commencement date. Confirmed registrants are to attend and complete all lectures, class exercises, workshops and assessments (where applicable). Additionally, all responses to feedbacks and surveys conducted by NUS-ISS and its partners must be submitted. All training and assessments will be delivered as described in the course webpage.

General Enquiry

Please feel free to write to ask-iss@nus.edu.sg if you have any enquiry or feedback.