Applied math and machine learning basics. Linear algebra
Probability and information theory
Numerical computation
Machine learning basics
Deep networks: modern practices. Deep feedforward networks
Regularization for deep learning
Optimization for training deep models
Convolutional networks
Sequence modeling: recurrent and recursive nets
Practical methodology
Applications
Deep learning research. Linear factor models
Autoencoders
Representation learning
Structured probabilistic models for deep learning
Monte Carlo methods
Confronting the partition function
Approximate inference
Deep generative models.