Deep Neural Networks - 605.742

This hands-on course provides a practical introduction to deep neural networks (DNNs), designed to deepen students’ understanding of advanced deep learning (DL) techniques. Modeled after the brain’s architecture, DNNs drive powerful applications in natural language processing (NLP), computer vision (CV), and speech processing—especially with unstructured data like text, images, video, and audio.The course begins with a four-week refresher on machine learning (ML), focusing on model evaluation and feature engineering using Python and Scikit-Learn (SKL). Prior experience with Python and SKL-based ML models is expected. From there, we transition to TensorFlow and Keras, exploring key DNN architectures including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), attention mechanisms, generative adversarial networks (GANs), deep reinforcement learning (DRL), transfer learning, and more. Students will read seminal DL papers and collaborate in teams on weekly Kaggle challenges to apply their skills. Teamwork is central to the course, which concludes with a final presentation based on a public ML/DL competition.

Course Prerequisite(s)

A course in Machine Learning