PDA

View Full Version : Chapter 1 - The Learning Problem


  1. Exercises and Problems
  2. question about probability
  3. Meaning of the variables y1,y2,..yN etc.?
  4. what is bias means?
  5. question target distribution
  6. ERRATA: p. 9
  7. A contradiction with Hoeffding?
  8. Data independence
  9. Does the Group Invariance Theorem for all linear threshold functions?
  10. Proof of Hoeffding's inequality is required
  11. How is linearity of PLA obvious?
  12. question about chap 1.4.1?
  13. PLA Optimization criteria
  14. Is the Hoeffding Inequality really valid for each bin despite non-random sampling?
  15. Hoeffding inequality for multiple classifiers
  16. Ch. 1.3.1 vs. Ch. 1.3.2
  17. Chapter 1 - Exercises 1.8/1.9
  18. role of P(X) ?
  19. Clarification needed for Hoeffding Inequality
  20. Interesting blog post relates Hoeffding Ineq to Frequentist Conf Intervals
  21. Hoeffding's Inequality vs. binomial distribution
  22. Perceptron: iterating weights
  23. Chapter 1 - Exercise 1.9
  24. Chapter 1 - Problem 1.3
  25. Stuck on Problem 1.7
  26. Problem 1.12a
  27. How to draw the separating line in 2D input
  28. does PLA works for the cases of Non-linear separable
  29. How to choose features
  30. Exercise 1.11
  31. Exercise 1.10
  32. Exercise 1.7
  33. Question on the Selection of Samples from the Bin
  34. E_in E_out trade-off, Couldn't E_in always be 0?
  35. Exercise 1.12 - Failing to make Ein(g) small enough
  36. Problem 1.11
  37. Exercise 1.13 noisy targets
  38. Hoeffding inequality for multiple hypothesis
  39. Exercise 1.1
  40. Problem 1.10 (b)
  41. Noisy target - problem in understanding distribution
  42. Ch 1: Question on f and h functions
  43. Problem 1.1
  44. Intuition of the step of PLA
  45. Exercise 1.10 part c
  46. Hoeffding Inequality
  47. Exercise 1.10
  48. Problem 1.9
  49. Problem 1.2 b
  50. Problem 1.3 c
  51. Perceptron update rule problem
  52. Noisy Target vs Target Distribution,Deterministic Target vs Deterministic Target Func
  53. Exercise 1.2
  54. Exercise 1.3 (b)
  55. I can't understand the aim of rating movies example
  56. Equation 1.3
  57. Noisy Targets as deterministic target function
  58. Hoeffding Inequality With Probability > 1?
  59. Exercise 1.10
  60. Can we guarantee there is a function with low in sample error in H
  61. Exercise 1.12
  62. Problem 1.8c
  63. P(x) vs. P(y |x)
  64. E(h,f), what is the entirety of h and f?
  65. Typo
  66. Problem 1.12 (b) and (c)
  67. Section 1.3 argument for feasibility of learning is fundamentally flawed
  68. Not Sure about What Exactly Question is Asking and Desired Answer Format
  69. problem 1.7