Diplomas
Diploma in Computer Vision
Teach machines to see — from image to decision
- On campus
- Hybrid
- Employment rate: 84%
Fees
- Duration
- 12 months
- Total hours
- 480 training hours
- Credit hours
- 32
- Next intake
- 22 February 2027
Overview
Computer vision is one of the clearest AI fields in terms of impact: a single camera can replace an entire manual inspection process. The diploma blends theoretical foundations with industrial application and tackles the problems short courses skip: changing lighting, awkward angles, and real-time performance on constrained hardware.
Manufacturing, security and healthcare lead regional demand for vision engineers.
Learning outcomes
- Build a complete visual pipeline from capture to decision
- Train detection and segmentation models on custom data
- Build a high-quality annotated dataset
- Deploy a vision model on a resource-constrained edge device
Curriculum
Semester 1 — Visual Processing Foundations
Semester 2 — Real Systems & Capstone
Capstone project
You build a vision system that genuinely runs: camera, model and automated decision. Examples from previous cohorts: production-line quality inspection, in-store product counting and classification, and safety equipment detection on a construction site.
Graduate projects galleryTarget roles
Admission requirements
- Basic Python programming background
- Passing the admission interview
- High school certificate or equivalent
Required documents
- ID or residency copy
- Latest qualification certificate
- Personal photo
Fees & instalments
26,000SAR
- Published fees include learning materials, assessment fees and the certificate.
- Diplomas can be paid in interest-free semester instalments; courses support monthly instalments via Tabby and Tamara.
- A merit scholarship of up to 30% for applicants with a high previous qualification average.
- mada
- Visa / Mastercard
- Apple Pay
- Tabby — instalments
- Tamara — instalments
- Bank transfer
Program faculty
Eng. Sara Alqahtani
Computer vision engineer who worked on visual inspection systems in industrial production lines, with published work in pattern recognition.
Eng. Faisal Aldosari
Experienced in running models that serve millions of requests daily; focuses on the gap between a notebook and a live service.
Frequently asked questions
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