Deep Learning

3 credits · Graduate School

This is the second course in a sequence exploring statistical modeling and machine learning techniques. Both courses emphasize unifying and high-level concepts such as prediction and calibration, classification and discrimination, loss functions, optimism and cross-validation, re-sampling methods for model assessment, the evaluation of modeling assumptions and bias-variance trade-off. This second course covers nonparametric regression, neural networks (convolution and recurrent), deep learning, reinforcement learning, long-short term memory models, hidden-markov models and Bayesian networks. [3]