Contents

In this course, we will cover basic concepts of statistical learning and explore their usage in a neuroscientific context.

• Exercise I: Exploratory Data Analysis (EDA)
• Exercise II: k-Nearest Neighbors (k-NN)
• Exercise III: Linear Regression
• Catching Up & Happy Birthday
• k-Nearest Neighbors (k-NN)
• Linear Regression
• Exercise IV: Logistic Regression
• Exercise V: Regularization
• Exercise VI: Revisiting Linear and Logistic Regression
• Exercise VII: Decision Trees and Random Forests
• Exercise VIII: K-Means Clustering and PCA
• Exercise IX: The Haxby Experiment (2001)
• Exercise X: Common Mistakes and Domain-Relevant Insights