Artificial Intelligence (AI)

This course brings a mix of theory and hands-on application for a dynamic learning experience.

Making AI

AI (Artificial Intelligence) class is hands-on, focusing on practical applications. Using Python libraries, students learn to train models. The class emphasizes the crucial role of data, teaching effective data curation methods.

Students explore diverse data acquisition methods, including crowdsourcing, prepackaged datasets, and automated techniques like web scraping or generative AI. They grasp the importance of quality data and the challenges and opportunities of different collection strategies.

The class covers computer vision libraries for object recognition. Students gain practical insights, developing skills in both the theory of AI and the hands-on aspects of working with data and computer vision. This approach ensures students are well-prepared for the complexities of AI in various domains.

This class is fairly new, so more project ideas are being created. Students that come in with ideas are welcomed. 

Common Questions.

Students that are interested in A.I. or have an idea in creating an A.I. to display or predict data.

Students will learn how to use sets of data and teach the A.I. how to interpret the data. 

Usually the student is recommended to create a GitHub account so they can upload their project and have access to it on any device they own to continue programming. Although mentors will not assign homework, it is recommended for the student to take the project home.