STK2100 - Maskinl?ring og statistiske metoder for prediksjon og klassifikasjon

Forelesninger / lectures

  • Lecture 1 (Introduction)
    • Chapter 1
    • Chapter 2 (§2.1 and §2.2)
    • R code
  • Lecture 2 (linear regression)
    • Chapter 3 (§3.1, §3.2)
    • R code
  • Lecture 3 (variable selection)
  • Lecture 4 (optimism, bias-variance trade-off)
  • Lecture 5 (cross-validation)
  • Lecture 6 (bootstrapping, AIC, BIC)
    • Chapter 7 (§7.4, §7.5, §7.7 (only up to page 233), §7.11)
    • R code
  • Lecture 7 (PCR, PLS)
  • Lecture 8 (regularized regression: lasso, ridge, elastic-net)
    • Chapter 3 (§3.4)
    • Chapter 18 (§18.4)
    • R code
  • Lecture 9 (kNN, curse of dimensionality)
    • Chapter 2 (§2.3.2, §2.3.3, §2.5)
    • R code
  • Lecture 10 (kernel smoothing methods)
    • Chapter 6 (§6.1, §6.2, §6.3)
    • R code
  • Lecture 11 (splines)
    • Chapter 5 (§5.1, §5.2)
  • Lecture 12 (splines, smoothing splines, thin-plate)
    • Chapter 5 (§5.2.1, §5.4 (only up to the first paragraph of page 154), §5.5)
    • R code
  • Lecture 13 (additive models)
    • Chapter 9 (§9.1, §9.2)
    • R code
  • Lecture 14 (trees, bagging)
    • Chapter 9 (§9.2 - except §9.2.3)
    • Chapter 8 (§8.7)
    • R code
  • Lecture 15 (random forests)
    • Chapter 15 (§15.1, §15.2, §15.3)
  • Lecture 16 (boosting, PPR, neural networks)
    • Chapter 10 (§10.1, §10.2, §10.3)
    • Chapter 11 (§11.1, §11.2, §11.3)
  • Lectures 17 and 18 (case study example: regression)
  • Lecture 19 (classification, linear regression of an indicator matrix)
    • Chapter 4 (§4.1, §4.2)
    • R code
  • Lecture 20 (logistic regression, linear discriminant analysis)
    • Chapter 4 (§4.3, §4.4)
    • R code
  • Lecture 21 (quadratic discriminant analysis, LDA vs logistic regression)
    • Chapter 4 (§4.3, §4.4.5)
    • R code
  • Lecture 22 (kNN -, trees -, boosting - for classification, Support vector machines)
    • Chapter 9 (§9.2.3)
    • Chapter 10 (§10.9)
    • Chapter 12 (§12.1 --  §12.3.1)
    • Chapter 13 (§13.3)
    • R code
  • Lecture 23 (support vector machines in the non separable case)
    • Chapter 12 (§12.2, 12.3.1)
    • R code
  • Lecture 24 (cluster analysis)
    • Chapter 14 (§14.3)
  • Lecture 25 (hierarchical and non-hierarchical clustering)
  • Lecture 26 (bagging for classification, case study example: classification)
  • Lecture 27 (case study example: clustering)
    • Ch 6.3 in "Data anlysis and data mining" by A.Azzalini and B.Scarpa, Oxford University Press, 2012 (ISBN 978-0-19-976710-6)
    • R code

 

Publisert 10. jan. 2024 16:09 - Sist endret 16. mai 2024 11:33
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