CSE695: Deep Learning

• Chia-Ping Chen

• Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning

• ☐ introduction
• ☐ linear algebra
• ☐ probability and information theory
• ☐ numerical computation
• ☐ machine learning basics
• ☐ deep feedforward networks
• ☐ regularization for deep learning
• ☐ optimization for training deep models
• ☐ convolutional networks
• ☐ sequence models: recurrent neural networks
• ☐ practical methodology
• ☐ applications
• ☐ linear factor models
• ☐ autoencoders
• ☐ representation learning
• ☐ structured probabilistic models for deep learning
• ☐ Monte Carlo methods
• ☐ partition functions
• ☐ approximate inference
• ☐ deep generative models

• probablity
• machine learning