Courses Detail Information

VE581 – Convolutional Neural Networks for Visual Recognition


Jiajia Luo

Credits: 3 credits

Pre-requisites: (MATH2140J Obtained Credit||MATH4170J Obtained Credit)&&(MATH2160J Obtained Credit||MATH2560J Obtained Credit||MATH2860J Obtained Credit)&&ECE2810J Obtained Credit


Computer vision has played an important role in many applications such as in search, image understanding, mapping, medicine, drones, and self-driving cars. For those applications, visual recognition is the key task. Recently, deep learning (neural network) technique has advanced the performance of these state-of-the-art visual recognition systems. This course will cover selected core topics on computer vision and deep learning, such as image classification, localization, and detection with convolutional neural network. Students will learn to design their own neural networks to solve real-world problems, for example, medical imaging diagnosis. Through this course, students are expected to gain both theoretical and practical skills in computer vision and deep learning.

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