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I am Active in E-Learning. Visit My Sample Lessons.

Useful Teaching Resources

People Related to Computers
Data Mining Related Teaching Resources such as PPT's, Notes, Quizzes, Video Lessons, Applets. To Go To Main Resource Page

Data Mining Research Data Sets

UC Irvine Machine Learning Repository
Carnegie Mellon Statlib Archive
DELVE Datasets:
MIT Broad Institute Cancer Datasets

Useful Video Lessons

Video Lessons developed by me on Weka. You may find Weka SW also there. Enjoy

Useful Teaching Applets

  1. Mining Massive RFID, Trajectory, and Traffic Data Sets

    Jiawei Han, Jae-Gil Lee, Hector Gonzalez, Xiaolei Li
    2 videos
  2. Binomial Distribution
  3. Occams Razor
  4. Principal Component Analysis Applet
  5. Some other Nearest Neighbour rules related applets
  6. Adaptive Principal Component Extraction
  7. PCA From other Learning Procedures
  8. Perceptron Learning
  9. Perceptron Learning
  10. Radial Basis Functions
  11. Gaussian Mixture Model
  12. Competetive Learning
  13. Support Vector Machines
  14. Multilayer Perceptron with binary input
  15. Multilayer Perceptron with Bi-polar input
  16. Prediction with Multilayer Perceptron
  17. OCR with Multilayer Pereceptron
  18. Backpropagation for Function Approximation
  19. Factor Reduction
  20. Least Squares Fitting
  21. Logistic regression
  22. Adaboost
  23. EM Algorithm
  24. Decision Surfaces in Knn Algorithm
  25. PCA Applet
  26. Bayesian Belief Networks
  27. Decision Tress
  28. Constraint Based Minimizaion
  29. Neural Networks
  30. Hidden Markov Models
  31. A Novel SOM-based approach for text mining
  32. JavaBayes
  33. Linear Programming Solution
  34. Linear Programming
  35. LP solver of Any Dimesion
  36. SVM
  37. Applet for PR
  38. Classifier
  39. Regression Applet
  40. SVM
  41. IEEE Resources
  43. Mixture Models
  44. Text classification Applet
  45. Text classification Applet
  46. 1-D SVM Regression
  47. SVM Applet
  48. Hierarchical Clustering Algorithm
  49. Fuzzy C Means Algorithms
  50. Another K-Meanss Applet With different distance metrics options
  51. K-NN Applet with multiple groups Ability
  52. Competetive Learning
  53. K-Means Clustering
  54. Bayesian Classifier
  55. Linear Classifier
  56. An Applet for Hypothesis Testing
  57. Simple 1-D Classification Applet
  58. Local Boosting Algorithms
  59. Pattern Recognition Applet with many classification algorithms
  60. SVM Applet
  61. Kohonen Maps
  62. Application of Normal Distribution
  63. Self Organising Maps
  64. ANOVA Apllet
  65. Correlation and Slope Intercept
  66. 2D Principal Componenets
  67. Least Absolute Deviation Regression
  68. Multicolinearity
  69. Polynomial Regression
  71. Version State Demo Applet
  72. Kernel Regression
  73. Minimum Spanning Tree Clustering
  74. Hypervolume Clustering Methods
  75. Genertic Algorithms
  76. Nearest Neighbour Applet
  77. Nearest Neighbour Classification
  78. Gaussian Process regression
  79. various Statistics tests such as t-test, Kolmogrov test, Chi Square testing,ANOVA test
  80. Least Squares Fitting
  81. Normal Distribution
  82. Z-Scores Demo.
  83. Normal Tables Demo
  84. Regression
  85. Central Limit Theorem Applet
  86. Bayesian Applet This applet is very attractive as it allows us to see the classes and how decision surface will effect with the change in their variance.
    1. Change X and Y standard deviations of class 1 and see how decision surface changes.
    2. Change X and Y standard deviations of class 2 and see how decision surface changes.
    3. Change X and Y standard deviations of class 1&2 such that they overlap and see how decision surface changes.
  87. Bayes Applet for Discrete Attributes.
  88. Competitive Learning

    • Elastic Net for TSP
      SOM and elastic nets can be regarded as competitive learning with a topological constraint.
      TSP is the most notorious one in the NP-complete problems. The definition is simple: Find the shortest closed-path through all points.
      (You can also see it here.)
    • Neural Competitive Models Demo
      Comparison between various kinds of competitive learning with topology reformation.
    • Bayesian Self-Organizing Maps (BSOM)
      SOM as a statistical model. Learning is regarded as an estimation algorithm for its parameters. Hyperparameters also are estimated. Ultimately a probability densty function for data is estimated.

    Backpropagation Learning

    Neural Nets for Constraint Satisfaction and Optimization

    Other Neural Networks

    Artificial Life

    Genetic Algorithm

    Biomorph & L-system

    Life Game


    Other AL

    AL collection

    Other Related Applets

Some Historical Machines

Prof.N.B. Venkateswarlu, Visakhapatnam, India

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