View Full Version : General Discussion of Machine Learning

  1. Using TeX in your post
  2. A Modification to the Learning Diagram
  3. Fitting fields/functions with NN
  4. Machine Learning
  5. Selecting Data for Training, Testing and Validation
  6. Support Vector Machines, Kernel Functions, Data Snooping
  7. Multi-Class Classifier
  8. AUC Metric
  9. Data Snooping, Classifiers
  10. SVM Research
  11. How to calculate VC dimension for matrix factorization (Netflix-like) tasks
  12. Types of Machine Learning
  13. Problem with simple perceptron implmenetation
  14. Dependent Data
  15. Normal equation in linear regression
  16. Spatial visualization in more than 3 dimensions
  17. Machine learning with vector images
  18. Congratulations Caltech!
  19. Signals predict
  20. computational complexity?
  21. Weather prediction
  22. Selecting "representative" test data
  23. Which kernel to use?
  24. Regression on hidden variables
  25. Polynomial regression
  26. Cross validation and scaling?
  27. Linear model in lecture 18
  28. SVM and C-parameter selection
  29. expected value for the SVM generalization error
  30. Covariate Shift in Data
  31. Neural network with discrete and continuous input
  32. Good Basic Books or courses for a Novice in Machine learining
  33. Learning Approach vs. Function Approximation
  34. Parallel machine learning
  35. Ideas of applications of machine learning in emacs
  36. How to handle ambiguous target function, f
  37. When to use normalization?
  38. General question on sampling bias
  39. cross validation and feature selection
  40. Neural Network predictions converging to one value
  41. How to get into Machine Learning Research
  42. Statistics vs. Machine Learning
  43. Machine Learning and census models
  44. Criss-cross validation
  45. kpca and svm
  46. Regression and Classification Problems
  47. Under-represented class data
  48. Try this!
  49. SVMs, kernels and logistic regression
  50. In-sample error and Max Likelihood
  51. Feature dimensionality, regularization and generalization
  52. Follow up reading on ML
  53. VC dimension of time series models
  54. Bayesian Model Combination
  55. ML tutoring help
  56. 'novel breakthrough theory of machine learning' by Vladimir Vapnik
  57. Looking for facts extraction tools
  58. Time Series method similarities
  59. example non-overlap hypothesis set H to make union bound equals vc bound
  60. What is the difference between machine learning and Learning from Data?
  61. The VC dimension, complexity, and hypothesis set
  62. A general learning problem
  63. Google TensorFlow has created a visual neural network learning tool
  64. A Question about machine learning
  65. Lecture Notes from Information and Complexity?
  66. Q: MacKay's method of setting weight decay
  67. How do we account for the grouping of the training data?
  68. applying machine learning?
  69. Best site for summary notes for the course!