Machine Learning

Supporting materials for a self-paced course on machine learning offered by IDSS.

Lessons

• Intro (course summary, setting up your environment, ...)
• Linear Regression
  • Model building and bias
  • Regression assumptions
  • Complex interactions
  • Missing data
• Inference
  • Confidence Intervals
  • Association tests
  • Power and sample size
  • Permutation tests
• Generalized linear models
  • Logistic regression
  • Odds ratios
  • Logistic regression assumptions
• Inference for high dimensional data
  • P-Hacking
  • Error rates
• Other machine learning topics
  • Principle Component Analysis
    • Application: Population structure
  • Clustering
  • Smoothing
  • Class prediction
  • Cross-validation
  • Intro to Deep Learning (could spend a lot of time on this...)