WhenWednesday, June 12
12:30 PM to 2:00 PM
- Teaching and Visualization Lab at the James B. Hunt Jr. Library
Mass Media – TV, advertising designed for mass communication was once one size fits all. However, today's commercials are customized by location. In fact, adverts on websites now strategize to seek engagement from users. By being interactive, registering user’s preferences and interests; transmitting these customized content to each user in a scalable and cost-effective manner; recommending the right product to the right person at the right time; tech giants have facilitated the supposedly inconceivable.
In recent times, when one logs into Netflix or Amazon, one immediately notices that both services have essentially analyzed items one has viewed and created recommendations based solely on one’s history. This is just a tip of the iceberg of data that can be used for personalized recommendation. With the recent craze over smart devices, wearable technology and internet of things, you are creating more and more data that tells the world what you do everyday, what you like, how much you’ve slept, what you like to eat, where you like to eat, etc.
In this workshop, we’ll learn about the algorithm behind most recommendation engines and implement a recommenders system using AI and machine learning. Bring your own laptop, you don’t want to be a dinosaur in the 21st century. Prior experience with Python is helpful and we strongly recommend installing Python 3 and Jupyter Notebook on your computer prior to the workshop.
Register for this workshop
- Ruth Okoilu