Introduction To Deep Learning

Catalog description

This course covers the theory and practice of deep learning (DL) for science. Students will gain a solid knowledge foundation for advanced DL algorithms, model architecture design, their real-world applications and implementations. They’ll receive guidance on building deep neural network models end-to-end from the ground up using the latest DL frameworks, including data collection, data enhancements, training strategies, and etc. In addition, students will work on hands-on projects in various areas like deep computer vision and modern natural language processing. Prerequisite(s): CS 33200 FOR LEVEL UG WITH MIN. GRADE OF C- AND CS 46900 FOR LEVEL UG WITH MIN. GRADE OF C- AND STAT 34500 FOR LEVEL UG WITH MIN. GRADE OF C- Course Learning Outcomes 1. Understand the principles and various classic and widely-applied algorithms of data mining and machine learning systems. 2. Design, implement and train machine learning models from the ground up. 3. Process raw data into suitable format for a range of data mining algorithms. 4. Engage in practical coursework through hands-on labs and projects. View Class Schedule