Description:
Lead instructor: Saurabh Bagchi, Professor Electrical & Computer Engineering, Computer Science, CRISP Center Director
Saurabh Bagchi is a Professor in the School of Electrical and Computer Engineering and the Department of Computer Science at Purdue University in West Lafayette, Indiana. He focuses on building robust computer systems, trying to break them by subjecting them to real-world constraints and failure modes, and improving the systems. He is the founding Director of the Purdue Center for Resilient Infrastructures, Systems, and Processes, CRISP, a center that works to provide scientific methods to analyze the failure modes of infrastructures and provide engineering tools to systematically build in resilience. Focus areas are cyberinfrastructure, cyber physical systems, and large-scale built structures.
Professor Bagchi was elected to membership in the International Federation for Information Processing (IFIP) (2020), received the Alexander von Humboldt Research Award (2018), the Adobe Faculty Award (2021, 2017), the AT&T Labs VURI Award (2016), the Google Faculty Award (2015), and the IBM Faculty Award (2014). Professor Bagchi is an IEEE Distinguished Visitor (2021) and an ACM Distinguished Scientist (2013). He serves on the IEEE Computer Society Board of Governors for the 2017-20 term.
Overview:
Course to provide focused training on state of the art machine learning algorithms, with particular emphasis on deep learning. The students will acquire a principled understanding for the various techniques that have a proven successful record in solving important engineering problems. Further, hands-on experimental training provided through the course projects.
Learning Outcomes
- Foundational material on reliability and security
- Data analytic techniques for dependability
- Big data security and insecurity
- Case Studies and challenge problems