Classification and Planned Experiments

Welcome to Classification and Planned Experiments. This course will first contrast regression models with classification models, which have broad application in machine learning. It will then introduce basic classification techniques, focusing on K-nearest neighbor, and logistic regression. You will examine data visualizations and see how setting hyperparameters or estimating parameters supports interpretation and effective classification. The course will then address another powerful field of applied statistics called experimental design, which is concerned with running controlled tests (experiments) to try to understand causal relationships between factors of interest. Several types of designs will be introduced, including ones that use computer modeling. You will learn the principles of experimental design and work through several examples to help you understand how to actually set up, run and analyze these experiments leveraging data

• Execute statistical classification techniques
• Understand principles of experimental design
• Conduct controlled experiments for causal analysis
• Analyze experimental data effectively
• Adjust parameters to improve classification accuracy

• Basic familiarity with statistical concepts and terminology
• Readiness for practical exercises