Machine Learning for Neuroscience
Introduction
Contents
Exercise I: Exploratory Data Analysis (EDA)
Exercise II: k-Nearest Neighbors (k-NN)
Exercise III: 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)
Resources
References
open issue
Index
