Deep Learning

Course Description

Deep learning is a branch of machine learning based on algorithms that try to model high-level abstract representations of data by using multiple processing layers with complex structures. Some representations make it easier to learn tasks (e.g., face recognition or spoken word recognition) from examples. One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature extraction. In this course students will study deep learning architectures such as autoencoders, , convolutional deep neural networks, and recurrent neural networks. They will read original research papers that describe the algorithms and how they have been applied to fields like computer vision, automatic speech recognition, and audio event recognition. For projects, students can work on their own or in groups (recommended) to write a codebase that reproduces a landmark research paper. This course is aimed at advanced undergraduates, masters, and PhD students. Prerequisites: CS 349 or CS PhD or Instructor permission.

Code

COMP_SCI 449-0

Credits

1

Subject

Computer Science

Programs That Include This Course

• Bachelor's Artificial Intelligence as a Second Major for McCormick Students
• Bachelor's Artificial Intelligence as a Second Major for non-McCormick Students
• Bachelor's Artificial Intelligence Major, BS in Engineering
• Bachelor's Computer Science Degree
• Bachelor's Computer Science Major
• Doctoral Applied Physics PhD
• Minor Machine Learning and Data Science Minor
• Undergraduate Certificate Robotics Certificate