Introduction to Deep Learning

Catalog description

This course provides a solid foundation in deep learning, a subset of machine learning that uses artificial neural networks with multiple layers to extract progressively higher-level features from data. Students will first explore core machine learning principles—including supervised and unsupervised learning, loss functions, optimization techniques, regularization, and evaluation metrics—before applying them to deep learning architectures. Through hands-on coding exercises, students will implement key concepts and solve real-world problems using advanced techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative models, and reinforcement learning. Applications span business analytics, computer vision, and natural language processing (NLP).