Common ML Methods
Used In Science
Machine learning is transforming how scientists uncover patterns, make predictions, and analyze complex systems. In this section, students will explore foundational ML methods—Linear Regression, Decision Trees, and Artificial Neural Networks—each with real-world applications in scientific research. Through hands-on activities, they will not only learn how these models work but also when and why to use them, helping them build a strong intuition for data-driven discovery.
Linear Regression
Linear regression is one of the most fundamental tools in data analysis and machine learning. It can help uncover relationships between variables and make predictions from data. Learning linear regression gives students a foundation for many types of scientific research and more advanced ML techniques.
Intro to Decision Trees
Decision trees are a supervised learning method used to classify data by making a series of binary decisions based on features. They will learn how features influence predictions, how training data affects accuracy, and how biases in data can shape outcomes. Through hands-on activities and interactive tools, students will build their understanding of how decision trees help make structured, data-driven decisions.
Neural Nets
Through an introductory mini-lecture and a hands-on simulation, students gain familiarity with how artificial neural networks function, the structure and training process of neural networks, and how these systems are used to interpret data and make predictions.