A survey of Deep Learning techniques and their application to problems in computer vision and data science. Deep learning techniques may include Deep Neural Networks, Convolutional Neural Networks, Recurrent Networks, Deep Generative Models and Reinforcement Learning. Application domains will focus on computer vision problems, including image classification, object detection and image segmentation. Additional application domains in natural language processing and robotics control will be introduced. Software tools will be introduced for practical application. Weekly hours: 3 Lecture hoursPrerequisite(s): CMPT 317.3; and one of STAT 242.3 (preferred), STAT 245.3 or EE 216.3; and one of MATH 164.3, MATH 266.3, or CE 318.3. Note: Students with credit for CMPT 828 or CMPT 498.3 Deep Learning and Applications may not take this course for credit. Costs in addition to tuition apply to this course.
A survey of Deep Learning techniques and their application to problems in computer vision and data science. Deep learning techniques may include Deep Neural Networks, Convolutional Neural Networks, Recurrent Networks, Deep Generative Models and Reinforcement Learning. Application domains will focus on computer vision problems, including image classification, object detection and image segmentation. Additional application domains in natural language processing and robotics control will be introduced. Software tools will be introduced for practical application. Weekly hours: 3 Lecture hoursPrerequisite(s): CMPT 317.3; and one of STAT 242.3 (preferred), STAT 245.3 or EE 216.3; and one of MATH 164.3, MATH 266.3, or CE 318.3. Note: Students with credit for CMPT 828 or CMPT 498.3 Deep Learning and Applications may not take this course for credit. Costs in addition to tuition apply to this course.
