Machine learning is a subfield of artificial intelligence (AI) that enables computers to learn models from data in order to perform tasks like classifications, recognitions, detections, etc . This is an introductory machine learning course aimed to help the audience build a solid foundation for developing AI applications and exploring more advanced topics.
The course will begin with an overview of machine learning and its applications. We will then focus on several popular machine learning methods, such as linear regression, decision trees, random forests, and neural networks, and explain how they work and when to use them. Some essential topics like overfitting, regularization, bias-variance trade-off, model evaluation will be addressed in the course.As the goal to help the audience to obtain practical skills in machine learning, we will run a list of hands-on exercises throughout the course to illustrate how to apply the aforementioned knowledge to solve real-world problems. The audience will have the opportunity to try some of the exercises on our clusters.
Prerequisites: Beginner’s level of Python is required. Knowledge/experience with Scikit-learn and Tensorflow are preferred but not required.This workshop will be delivered online in two sessions:
- June 22 from 9:00 A.M. to 12:00 P.M. Eastern Daylight Time
- June 22 from 1:30 P.M. to 4:30 P.M. Eastern Daylight Time
- Teacher: Weiguang Guan