IEEE Workshops on Machine Learning and Convolutional Neural Networks

Tuesday, May 30th, 2017 at 4:30 PM

TI Auditorium


Workshop 1: May 30th (Tuesday)
4:30 PM-5.00 PM (PT) Check In/Networking/Refreshments,
5:00 PM-9.00 PM Workshop 1

Workshop 2: May 31st (Wednesday)
4:30 PM- 5.00 PM (PT) Check In/Networking/Refreshments,
5:00 PM-9.00 PM Workshop 2



Session Abstract:As data sources proliferate along with the computing power to process them, going straight to the data is one of the most straightforward ways to quickly gain insights and make predictions. Machine learning brings together computer science and statistics to harness that predictive power. Itís a must-have skill for all aspiring data analysts and data scientists, or anyone else who wants to wrestle all that raw data into refined trends and predictions.

This is a training that will teach you the end-to-end process of investigating data through a machine-learning lens. It will teach you how to extract and identify useful features that best represent your data, a few of the most important machine learning algorithms, and how to evaluate the performance of your machine learning algorithms. In this short course, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical knowledge needed to quickly and powerfully apply these techniques to new problems.

This series of workshops are focused on explaining the foundations and intuitions of machine learning along with guided programming exercises. It describes deep learning techniques used by practitioners in industry, including classic machine learning techniques, deep convolutional neural networks, regularization, optimization algorithms, and practical methodology with focus on guided examples in computer vison applications.

Speaker: Dr. Kiran Gunnam

Bio: Dr. Kiran Gunnam is working as a technical director of algorithms and signal processing at Velodyne LiDAR, Inc. He is leading the development of machine learning, simultaneous localization and mapping (SLAM) and signal processing algorithms and real-time hardware implementation for LiDAR sensor based self-driving cars.

Dr. Gunnam is an innovative technology leader with vision and passion who effectively connects with individuals and groups. Dr. Gunnam's breakthrough contributions are in the areas of advanced error correction systems, storage class memory systems and vision based navigation systems. He has helped drive organizations to become industry leaders through ground-breaking technologies. Dr. Gunnam has 70 issued patents and 100+ patent applications/invention disclosures on algorithms, computing and storage systems. He is the lead inventor/sole inventor for 90% of them. Dr. Gunnamís patented work has been already incorporated in more than 2 billion data storage and WiFi chips and is set to continue to be incorporated in more than 500 million chips per year.

Dr. Gunnam served as IEEE Distinguished Speaker and Plenary Speaker for 19 events and international conferences and more than 2000 attendees in USA, Canada and Asia benefited from his lecture talks. He also teaches graduate level course focused on machine learning systems at Santa Clara University.