IEEE Workshops on Machine Learning, Convolutional Neural Networks and Tensorflow

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. It’s 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.

Workshop 1: Machine Learning Foundations -An Intuitive Approach

This first workshop offers an intuitive treatment of the important machine learning approaches. The workshop covers supervised Learning and unsupervised learning. Various classic machine learning as well as modern deep neural networks and deep belief networks are covered. How to build an end-to-end application is covered in depth focusing on selecting right machine learning algorithm, data preprocessing and evaluating model.

Workshop 2: Deep learning with CNN and Tensorflow

This second workshop offers an in-depth treatment of Convolutional neural networks (CNN) and explain each layer in detail. It also covers various architecture optimization techniques including data optimization, drop outs, layer patterns and sizing. It provides a comprehensive case study of recent CNN architectures including AlexNet, ZFNet and GoogleNet.

Tensorflow basics would be covered. Guided exercises in Tensorflow involve programming linear regression and nearest neighbors approaches, building a simple neural network for XOR, building CNN for handwritten digits recognition. Template software functions are provided with most of the software is written except for the key concepts. Instructor will work with attendees to help them complete the solutions.

Attendees for workshop 2 should bring their own laptop with Tensorflow installed. Installation links:

https://www.tensorflow.org/

Discounts are available for IEEE Members,sponsoring chapter members and Texas Instruments Employees.

If you are attending both the days of the workshop, you can buy the combined ticket instead of buying two separate tickets for each day.

Registrations close on September 18th, one week before the workshop.

• 4:00 PM- 4.15 PM (PT) Check In/Networking/Refreshments,
• 4:15 PM-9.00 PM Workshop 2