This course offers students an introduction to some of the latest state-of-the-art techniques in the field of deep learning. Throughout the course, students will delve into a wide range of advanced topics and methodologies, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative models such as large language models, and transformers, among others. The primary focus will be on comprehending the underlying principles and mathematical foundations of these techniques, as well as their practical applications in domains such as computer vision and natural language processing. The course places strong emphasis on practical implementations, allowing students to gain hands-on experience by implementing these techniques themselves. By the end of the course, students will have a basic understanding of deep learning, equipping them with the confidence to apply cutting-edge techniques to real-world problems.
Prerequisites: Students need to grasp knowledge in Linear algebra, Calculus, Probability and Statistics, basic data structure and algorithms, and significant experience in computer programming (python in particular). CMPSC 165B is a prerequisite.