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Apr 20, 2026
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DS 4013 Applications of Statistical Learning and Neural
(3 hours)
The course introduces the main concepts of machine learning and statistical pattern recognition such as training, validation, and testing statistical models as well as regression, classification, overfitting, and different types of learning problems. Various neural network architectures (e.g., feed-forward and recurrent) are introduced and implemented using Python 3 packages including Tensorflow and Keras. Also, neural network learning methods are discussed including the gradient descent-based approaches with stochastic and batch algorithms, as well as the generative approaches (e.g., contrastive divergence and Restricted Boltzmann Machines). Applications of neural networks in decision making tasks, time series prediction, image classification, object detection, and reinforcement learning are discussed. Prerequisite: CS 3083 /AI 3083. Same as AI 4013
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