Last semester at University of Vermont as a teaching assistant.
Formal languages and expressiveness. Turing completeness and Church’s Thesis. Decidability and tractability. Complexity classes and theory of NP completeness.
CS 395 Deep Learning (graduate course)
Connectionist architectures commonly associated with deep learning, e.g., basic neural networks, convolutional neural networks, auto-encoders, deep belief networks, recurrent neural networks and generative adversarial networks.
Train and optimize the architectures using open source software libraries such as Caffe, keras and Tensorflow.