Welcome to psy112!

This book contains the course contents for the Statistical Learning Seminar (psy112). We will explore and discuss the contents of the lecture in a practical way using Python.

Table of contents

Machine Learning Basics

• Recap: Regression Models
• What is Machine Learning?
• Bias-Variance Tradeoff
• Resampling Strategies
• Exercises

Modelling

• Model Selection
• Regularization
• Generalized Additive Models
• K-Nearest Neighbors
• LDA & QDA
• Naïve Bayes
• Support Vector Machines
• Decision Trees
• Neural Networks
• Principal Component Analysis
• Exercises

Additional Materials

• Exercise solutions

Additional resources

• https://www.statlearning.com/

• https://animlbook.com/

• https://geostatsguy.github.io/MachineLearningDemos_Book