This course aims to provide a comprehensive introduction to the foundational and practical aspects of Deep Learning and Generative AI. Through a balanced blend of theoretical concepts and hands-on experience, students will learn to build, train, and evaluate artificial neural networks for a variety of tasks in computer vision and natural language processing. The course covers key architectures such as Convolutional Neural Networks (CNNs) for image data, Recurrent Neural Networks (RNNs) and LSTMs for sequential data, and extends into the realm of generative models including Autoencoders, Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs) and Large Language Models (LLMs). By the end of the course, learners will gain the skills to implement core deep learning models and apply generative AI techniques to solve practical problems.
For details of standard term assessment timelines and exam structures, visit our Academics page.
Professor , Department of Mechanical Engineering, Wadhwani School of AI , IIT Madras
Balaji Srinivasan is a Professor at the Wadhwani School of AI and Dept. of Mechanical Engineering at IIT-Madras. He has a PhD from Stanford (2005), MS from Purdue, B.Tech from IITM. His current research interests are in Scientific Machine Learning, Numerical solution of PDEs and Applied Deep Learning.
Professor , Wadhwani School of AI , IIT Madras
Ganapathy Krishnamurthi is a Professor at the Wadhwani School of AI. He has a PhD from Purdue University (2008), MSc (Physics) from IITM. His current research interests are in Generative AI, and Deep learning applied to Medical Image Analysis and Medical Image Reconstruction.