Computer Vision
About This Course
This course introduces the core principles of machine learning based computer vision, focusing on image analytics and technologies for detection, recognition, and classification tasks. Learners will explore the mathematical foundations underpinning these techniques and gain practical experience through case studies and real-world projects. The curriculum covers essential algorithms, feature extraction, and model design strategies for building effective vision systems. By the end of the course, learners will be equipped to design and implement machine learning based computer vision solutions to address diverse real-world challenges in areas such as object detection, image recognition, and automated classification.
What You'll Learn
1. Identify various machine learning based computer vision technologies.
2. Explain how different machine learning technologies are used and implemented in various computer vision problems.
3. Describe the basic image analytics concepts and technology including image filtering and features.
4. Analyse state-of-the-art computer vision models (e.g., Convolutional Neural Networks (CNNs), object detection frameworks) and evaluate their strengths, limitations, and applicability to real world tasks.
5. Design and implement computer vision systems for detection, recognition, and classification tasks using modern deep learning techniques.
Entry Requirements
No pre-requisites are needed to enroll in the individual courses; however, learners without qualifications or work experience in relevant engineering or the related fields may find the course contents challenging. Prior knowledge in STEM field will be an advantage.