2. CSE343/543 Machine Learning Mayank Vatsa Lecture slides are prepared using several teaching resources and no authorship is claimed for any slides.

3. The Perceptron  Binary classifier functions  Threshold activation function

4. What is here to learn? The Perceptron Training Rule

5. The Perceptron: Threshold Activation Function  Binary classifier functions  Threshold activation function

6.  One way to learn an acceptable weight vector is to begin with random weights  Then iteratively apply the perceptron weights to each training example  Modifying the perceptron weights whenever it misclassifies an example  This process is repeated, iterating through the training examples as many times as needed until the perceptron classifies all training examples correctly.  Weights are modified at each step according to the perceptron training rule, which revises the weight associated with input The Perceptron Training Rule

7. Assuming the problem is linearly separable, there is a learning rule that converges in a finite time Motivation A new (unseen) input pattern that is similar to an old (seen) input pattern is likely to be classified correctly The Perceptron Training Rule

8.  Basic Idea – go over all existing data patterns, whose labeling is known, and check their classification with a current weight vector  If correct, continue  If not, add to the weights a quantity that is proportional to the product of the input pattern with the desired output Z (1 or –1) The Perceptron Training Rule

9. Weight Update Rule

10.  The delta training rule is best understood by considering the task of training an unthresholded perceptron; that is, a linear unit for which the output o is given by Gradient Descent and Delta Rule

11. In order to derive a weight learning rule for linear units, let us begin by specifying a measure for the training error of a hypothesis (weight vector), relative to the training examples.

12. Derivation GDR The vector derivative is called the gradient of E with respect to The gradient specifies the direction that produces the steepest increase in E. The negative of this vector therefore gives the direction of steepest decrease. The training rule for gradient descent is

13. Derivation of GDR … The negative sign is presented because we want to move the weight vector in the direction that decreases E. This training rule can also written in its component form which makes it clear that steepest descent is achieved by altering each component of in proportion to.

14. The vector of derivatives that form the gradient can be obtained by differentiating E The weight update rule for standard gradient descent can be summarized as Derivation of GDR …

15. AB problem

16. XOR problem

17. 1

18. 2

20. 1 1

21. 2 2

22. 1 2 AND

23. xnxn x1x1 x2x2 Input Output Three-layer networks Hidden layers

24. Feed-forward layered network Output layer 2 nd hidden layer 1 st hidden layer Input layer

25. Different Non-Linearly Separable Problems Structure Types of Decision Regions Exclusive-OR Problem Class Separation Most General Region Shapes Single-Layer Two-Layer Three-Layer Half Plane Bounded By Hyperplane Convex Open Or Closed Regions Arbitrary (Complexity Limited by No. of Nodes) A AB B A AB B A AB B B A B A B A

26. In the perceptron/single layer nets, we used gradient descent on the error function to find the correct weights:  w ji = (t j - y j ) x i We see that errors/updates are local to the node i.e. the change in the weight from node i to output j (w ji ) is controlled by the input that travels along the connection and the error signal from output j x1x1 (t j - y j )

27. x1x1 x2x2 But with more layers how are the weights for the first 2 layers found when the error is computed for layer 3 only? There is no direct error signal for the first layers!!!!! ?

28. Objective of Multilayer NNet x1x1 x2x2 xnxn w1w1 w2w2 wmwm x = Training set Goalfor all k

29. Learn the Optimal Weight Vector w1w1 w2w2 wmwm x1x1 x2x2 xnxn x = Training set Goalfor all k

30. First Complex NNet Algorithm  Multilayer feedforward NNet

31. Training: Backprop algorithm  Searches for weight values that minimize the total error of the network over the set of training examples.  Repeated procedures of the following two passes:  Forward pass: Compute the outputs of all units in the network, and the error of the output layers.  Backward pass: The network error is used for updating the weights (credit assignment problem).  Starting at the output layer, the error is propagated backwards through the network, layer by layer. This is done by recursively computing the local gradient of each neuron.

32.  Back-propagation training algorithm illustrated:  Backprop adjusts the weights of the NN in order to minimize the network total mean squared error. Network activation Error computation Forward Step Error propagation Backward Step

33. Learning Algorithm: Backpropagation Learning Algorithm: Backpropagation Pictures below illustrate how signal is propagating through the network, Symbols w (xm)n represent weights of connections between network input x m and neuron n in input layer. Symbols y n represents output signal of neuron n.

34. Learning Algorithm: Backpropagation Learning Algorithm: Backpropagation

36. Propagation of signals through the hidden layer. Symbols w mn represent weights of connections between output of neuron m and input of neuron n in the next layer.

37. Learning Algorithm: Backpropagation Learning Algorithm: Backpropagation

39. Propagation of signals through the output layer.

40. Learning Algorithm: Backpropagation Learning Algorithm: Backpropagation In the next algorithm step the output signal of the network y is compared with the desired output value (the target), which is found in training data set. The difference is called error signal d of output layer neuron

41. Learning Algorithm: Backpropagation Learning Algorithm: Backpropagation The idea is to propagate error signal d (computed in single teaching step) back to all neurons, which output signals were input for discussed neuron.

42. Learning Algorithm: Backpropagation Learning Algorithm: Backpropagation The idea is to propagate error signal d (computed in single teaching step) back to all neurons, which output signals were input for discussed neuron.

43. Learning Algorithm: Backpropagation Learning Algorithm: Backpropagation The weights' coefficients w mn used to propagate errors back are equal to this used during computing output value. Only the direction of data flow is changed (signals are propagated from output to inputs one after the other). This technique is used for all network layers. If propagated errors came from few neurons they are added. The illustration is below:

44. Learning Algorithm: Backpropagation Learning Algorithm: Backpropagation When the error signal for each neuron is computed, the weights coefficients of each neuron input node may be modified. In formulas below df(e)/de represents derivative of neuron activation function (which weights are modified).

45. Learning Algorithm: Backpropagation Learning Algorithm: Backpropagation When the error signal for each neuron is computed, the weights coefficients of each neuron input node may be modified. In formulas below df(e)/de represents derivative of neuron activation function (which weights are modified).

46. Learning Algorithm: Backpropagation Learning Algorithm: Backpropagation When the error signal for each neuron is computed, the weights coefficients of each neuron input node may be modified. In formulas below df(e)/de represents derivative of neuron activation function (which weights are modified).

47. y 11 11 22 22 mm mm x1x1 x2x2 xnxn w1w1 w2w2 wmwm x = Single-Hidden Layer NNet Hidden Units

48. y 11 11 22 22 mm mm x1x1 x2x2 xnxn w1w1 w2w2 wmwm x = Radial Basis Function Networks Hidden Units

49. Non-Linear Models Adjusted by the Learning process Weights

50. Typical Radial Functions  Gaussian  Hardy Multiquadratic  Inverse Multiquadratic

51. Gaussian Basis Function (  =0.5,1.0,1.5)

52. + + Most General RBF +

53. The Topology of RBF NNet Feature Vectors x1x1 x2x2 xnxn y1y1 ymym Inputs Hidden Units Output Units Subclasses Classes

54. Radial Basis Function Networks x1x1 x2x2 xnxn w1w1 w2w2 wmwm x = Training set Goalfor all k

55. Learn the Optimal Weight Vector w1w1 w2w2 wmwm x1x1 x2x2 xnxn x = Training set Goalfor all k

56. Regularization Training set Goalfor all k If regularization is not needed, set

57. Learn the Optimal Weight Vector Minimize

58. Learning Kernel Parameters x1x1 x2x2 xnxn y1y1 ymym 11 22 ll w 11 w 12 w1lw1l wm1wm1 wm2wm2 w ml Training set Kernels

59. What to Learn?  Weights w ij ’s  Centers  j ’s of  j ’s  Widths  j ’s of  j ’s  Number of  j ’s x1x1 x2x2 xnxn y1y1 ymym 11 22 ll w 11 w 12 w1lw1l wm1wm1 wm2wm2 w ml

60. Two-Stage Training x1x1 x2x2 xnxn y1y1 ymym 11 22 ll w 11 w 12 w1lw1l wm1wm1 wm2wm2 w ml Step 1 Step 2 Determines  Centers  j ’s of  j ’s.  Widths  j ’s of  j ’s.  Number of  j ’s. Determines  Centers  j ’s of  j ’s.  Widths  j ’s of  j ’s.  Number of  j ’s. Determines w ij ’s.

61. Learning Rule  Backpropagation learning rule will apply in RBF also.

62. Three-layer RBF neural network

63. Deep NNet

67. Deep Supervised Learning

69. Convolution NNet – Example of DNNet

70. Next Class – Deep Learning (CNN)