Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods

 

 

 

Plan for week 47

  • Work and Discussion of project 3
  • Second last weekly exercise,
  1. Basics of decision trees, classification and regression algorithms and ensemble models
  2. Readings and Videos:
    1. These lecture notes at https://github.com/CompPhysics/MachineLearning/blob/master/doc/pub/week47/ipynb/week47.ipynb
    2. See also lecture notes from week 46 at https://github.com/CompPhysics/MachineLearning/blob/master/doc/pub/week46/ipynb/week46.ipynb. The lecture on Monday starts with a repetition on how to make a decision tree.
    3. Video of lecture at https://youtu.be/RIHzmLv05DA
    4. Whiteboard notes at https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesNovember18.pdf
    5. Video on Decision trees https://www.youtube.com/watch?v=RmajweUFKvM&ab_channel=Simplilearn
    6. Video on boosting methods https://www.youtube.com/watch?v=wPqtzj5VZus&ab_channel=H2O.ai
    7. Video on AdaBoost https://www.youtube.com/watch?v=LsK-xG1cLYA
    8. Video on Gradient boost, part 1, parts 2-4 follow thereafter https://www.youtube.com/watch?v=3CC4N4z3GJc
    9. Decision Trees: Rashcka et al chapter 3 pages 86-98, and chapter 7 on Ensemble methods, Voting and Bagging and Gradient Boosting. See also lecture from STK-IN4300, lecture 7 at https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf.