1. Machine Learning and Neural Networks Riccardo Rizzo Italian National Research Council Institute for Educational and Training Technologies Palermo - Italy

9. k-NN algorithm c 2 c c1 c4 c3 c4 c1 c2 c2 c3 c4 1 New input Inputs already classified Class 1

23. Decision trees T 1 T 3 T 2 T 4 1 2 1 3 2 1 3 classes 4 tests (maybe 4 variables)

57. Artificial Neuron x 1 x 2 x n w 1j w 2j w nj y j b j

59. Neural Networks 1. Learning stage 2. Test stage (working stage) Your knowledge is useless !!

68. Perceptron x 1 x 2 x x x x x x x x x

72. Linear Units x 2 x n w 1j w 2j w nj b j Y j =s j

74. The Delta Rule 2 (1)

76. Backpropagation . . . x 1 x 2 x n v jk h j w ij y i

83. Backpropagation

84. Backpropagation

86. Backpropagation . . . x 1 x 2 x n v jk h j w ij y i

87. Backpropagation . . . x 1 x 2 x n v jk h j w ij y i

90. Hopfield Network

97. Hopfield Networks Stable state State state Input

106. Kohonen Maps

107. Kohonen Maps The input x is given to all the units at the same time

108. Kohonen Maps The weights of the winner unit are updated together with the weights of its neighborhoods