Fundamentals of deep learning, including architectures (e.g., MLPs, CNNs, RNNs, Transformers, and GNNs) and learning algorithms under different paradigms (supervised / unsupervised / reinforcement learning). Emphasis on design principles and motivating applications. Recommended pre-requisite: CPEN_V 355 or CPSC_V 340. [3-0-2] Prerequisite: One of MATH_V 152, MATH_V 221 and one of MATH_V 318, MATH_V 302, STAT_V 302, STAT_V 321, ELEC_V 321 and one of CPEN_V 221, CPEN_V 223, CPSC_V 259. This cou...
Fundamentals of deep learning, including architectures (e.g., MLPs, CNNs, RNNs, Transformers, and GNNs) and learning algorithms under different paradigms (supervised / unsupervised / reinforcement learning). Emphasis on design principles and motivating applications. Recommended pre-requisite: CPEN_V 355 or CPSC_V 340. [3-0-2] Prerequisite: One of MATH_V 152, MATH_V 221 and one of MATH_V 318, MATH_V 302, STAT_V 302, STAT_V 321, ELEC_V 321 and one of CPEN_V 221, CPEN_V 223, CPSC_V 259. This cou...
