Brett Lantz

With machine learning, it is often difficult to make the leap from classroom examples to the real-world. Real-world applications often present challenges that require more advanced approaches for preparing, exploring, modeling, and evaluating the data. The goal of this course is to prepare students to independently apply machine learning methods to their own tasks. We will cover the practical techniques that are not often found in textbooks but discovered through hands-on experience. We will practice these techniques by simulating a machine learning competition like those found on Kaggle (https://www.kaggle.com/). The target audience includes students who are interested in applying ML knowledge to more difficult problems and learning more advanced techniques to improve the performance of traditional ML methods.

Machine learning, put simply, involves teaching computers to learn from experience, typically for the purpose of identifying or responding to patterns or making predictions about what may happen in the future. This course is intended to be an introduction to machine learning methods through the exploration of real-world examples. We will cover the basic math and statistical theory needed to understand and apply many of the most common machine learning techniques, but no advanced math or programming skills are required. The target audience may include social scientists or practitioners who are interested in understanding more about these methods and their applications. Students with extensive programming or statistics experience may be better served by a more theoretical course on these methods.