Deep Learning and Neural Networks

Catalog description

Introduces students to Bayesian statistics and probabilistic programming to model uncertainty and decision-making in AI. Students will explore key concepts such as training techniques, optimization strategies, and model evaluation, while progressing through perceptron-based models to advanced architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Includes both theory and practical application such as pattern/voice, word recognition, handwriting, and facial recognition to ensure proficiency in implementing neural network architectures using popular AI libraries.