Practical class materials:

• P1: Univariate data analysis
  • solution notes
    
• P2: Decision trees
  • solution notes
    
• P3: Bayesian learning
  • solution notes
    
• P4: Linear regression, kNN
  • solution notes (errata: 6 key {T,F,T,T})
    
• P5-6: Grandient descent
  • solution notes
    
• P7-8: Neural networks v2
  • solution notes v2 (all exercises; 2a notation updated)
    
• P9: Clustering
  • solution notes (3 updated; 4 notation updated)
    
• P10: Dimensionality reduction
  • solution notes
    
• (uncovered) RBFs and SVMs