Deep Learning. 3 credits

Catalog description

This course covers an introduction to neural networks and deep learning. The course covers multi-layer neural networks, convolutional neural networks, recurrent neural networks and transformers. The concepts of self-supervised, supervised, contrastive and reinforcement learning are introduced. Advanced topics on generative models both for images and language and associated applications are discussed. The course covers basics of optimization techniques, commonly used objectives and associated evaluation methodologies for different classes of problems. We discuss representative models and techniques for image classification, image and text generation, natural language processing and issues of robustness, interpretability and fairness. The focus is on practical skills for implementing and deploying new and existing models in real-world settings. Offered by Computer Science . Limited to two attempts.