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View Full Version : Chapter 3 - The Linear Model


  1. Exercises and Problems
  2. PLA vs Linear Regression
  3. Linear Regression - Statistics vs Data Mining
  4. Minor typos
  5. Coping with errors in training set
  6. Linear Regression and x0
  7. Linear regression - constructing X matrix
  8. LRA -> PLA Effect of Alpha
  9. On the 'linear model I'',one step learning in not what linear...
  10. Clarification about (non)linear regression
  11. Recency weighted regression
  12. Clarification of Problem 3.1
  13. Questions about Symmetry and Average Intensity of Zip Code digit
  14. What the hint of Problem 3.6(b)
  15. Classifying Handwritten Digits: 1 vs. 5
  16. Typo in Problem 3.16?
  17. About the Problem 3.17b
  18. Problem 3.3 Typo
  19. Problem 3.7d
  20. Feature Transform
  21. The trap of local minimum/maximum for linear regression
  22. Linear regression question
  23. Triangular numbers
  24. Pocket Algorithm and Proof of convergence
  25. Gradient Descent on complex parameters (weights)
  26. Exercise 3.4
  27. Exercise 3.15
  28. Problem 3.8
  29. Problem 3.2
  30. Problem 3.6
  31. Question about prob 3.17(a)
  32. Exercise 3.4
  33. Out of sample error for linear noisy target
  34. Problem 3.5
  35. Exercise 3.4(a)
  36. Pocket Algorithm
  37. Problem 3.18
  38. Problem 3.16
  39. Problem 3.15
  40. Problem 3.16
  41. invalid points in Z transform
  42. A question about HW6 of Prof. Magdon's class
  43. Pocket Algorithm
  44. data snooping
  45. Clarification of Conditional Probability Interpretation