Event box

Demystifying Machine Learning

Demystifying Machine Learning

Over the past decade, Machine Learning has been increasingly gaining popularity as a major buzz word in academic and industrial communities. Machine Learning has been at the core of many of today’s applications and services, from consumer products to large research projects. Despite its popularity, there is a lot of uncertainty to many about what exactly it is; how it is different from other technologies; what are the limits of its applications, etc. This course aims to explore these questions without going too much into the technical details of how machine learning works. The course will cover how to know when and why to consider using a machine learning solution and will include hands-on training on using a free no-coding tool, Weka, for Machine Learning prototyping.

This is an introductory course; no background is necessary. You will need access to a computer to participate in the activities; instructions on software installation will be sent in advance.

If you need credit for this workshop through the PDN for the computer refresh program, please register here and at https://profdev.tlos.vt.edu/; contact ndporter@vt.edu if you can't find the course listed.

If you are an individual with a disability and desire an accommodation, please email library-event-accessibility-g@vt.edu during regular business hours at least 10 business days prior to the event.

[Image by Enoch Lau - (screenshot of GPL software), GPL, https://commons.wikimedia.org/w/index.php?curid=1982855]

Thursday, October 22, 2020
10:00am - 1:00pm
Blacksburg Campus
    Alumni       Beginners       Faculty/Staff       Graduate Students       Public       Undergraduates  
    Workshop > Data Science       Workshop  

Registration is required. There are 12 seats available.

Event Contact

Profile photo of Chreston Miller
Chreston Miller

Data & Informatics Consultant

Amr Hilal

Head, Informatics Lab

Profile photo of Nathaniel Porter
Nathaniel Porter

Social Science Data Consultant and Data Education Coordinator