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CSC 4510 – Machine Learning Dr. Mary-Angela Papalaskari Department of Computing Sciences Villanova University Course website: 5: Multivariate Regression 1 CSC M.A. Papalaskari - Villanova University T he slides in this presentation are adapted from: Andrew Ng’s ML course

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Regression topics so far Introduction to linear regression Intuition – least squares approximation Intuition – gradient descent algorithm Hands on: Simple example using excel How to apply gradient descent to minimize the cost function for regression linear algebra refresher CSC M.A. Papalaskari - Villanova University 2

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What’s next? Multivariate regression Gradient descent revisited – Feature scaling and normalization – Selecting a good value for α Non-linear regression Solving for analytically (Normal Equation) Using Octave to solve regression problems CSC M.A. Papalaskari - Villanova University 3

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Size (feet 2 )Number of bedrooms Number of floors Age of home (years) Price ($1000) What’s next? We are not in univariate regression anymore: 4 CSC M.A. Papalaskari - Villanova University

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Andrew Ng Size (feet 2 )Number of bedrooms Number of floors Age of home (years) Price ($1000) …………… Multiple features (variables). CSC M.A. Papalaskari - Villanova University 5

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Andrew Ng Size (feet 2 )Number of bedrooms Number of floors Age of home (years) Price ($1000) …………… Multiple features (variables). Notation: = number of features = input (features) of training example. = value of feature in training example. CSC M.A. Papalaskari - Villanova University 6

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Andrew Ng Size (feet 2 )Price ($1000) …… Multiple features (variables). CSC M.A. Papalaskari - Villanova University 7

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8 For convenience of notation, define. Multivariate linear regression Hypothesis: Previously: Now:

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CSC M.A. Papalaskari - Villanova University 9 Hypothesis: Cost function: Parameters: (simultaneously update for every ) Repeat Gradient descent:

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CSC M.A. Papalaskari - Villanova University 10 (simultaneously update ) Gradient Descent Repeat Previously (n=1):

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CSC M.A. Papalaskari - Villanova University 11 (simultaneously update ) Gradient Descent Repeat Previously (n=1): New algorithm : Repeat (simultaneously update for )

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CSC M.A. Papalaskari - Villanova University 12 (simultaneously update ) Gradient Descent Repeat Previously (n=1): New algorithm : Repeat (simultaneously update for )

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CSC M.A. Papalaskari - Villanova University 13 E.g. = size ( feet 2 ) = number of bedrooms (1-5 ) Feature Scaling Idea: Make sure features are on a similar scale. size (feet 2 ) number of bedrooms Get every feature into range

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CSC M.A. Papalaskari - Villanova University 14 E.g. = size ( feet 2 ) = number of bedrooms (1-5 ) Feature Scaling Idea: Make sure features are on a similar scale. Replace with to make features have approximately zero mean (Do not apply to ). Mean normalization E.g.

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CSC M.A. Papalaskari - Villanova University 15 Gradient descent -“Debugging”: How to make sure gradient descent is working correctly. -How to choose learning rate.

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CSC M.A. Papalaskari - Villanova University 16 No. of iterations Making sure gradient descent is working correctly. -For sufficiently small, should decrease on every iteration. -But if is too small, gradient descent can be slow to converge. Declare convergence if decreases by less than in one iteration?

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CSC M.A. Papalaskari - Villanova University 17 Summary: Choosing -If is too small: slow convergence. -If is too large: may not decrease on every iteration; may not converge. To choose, try

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Andrew Ng Housing prices prediction CSC M.A. Papalaskari - Villanova University 18

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Andrew Ng Polynomial regression Price (y) Size (x) CSC M.A. Papalaskari - Villanova University 19

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Andrew Ng Choice of features Price (y) Size (x) CSC M.A. Papalaskari - Villanova University 20

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Andrew Ng Gradient Descent Normal equation: Method to solve for analytically. CSC M.A. Papalaskari - Villanova University 21

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Andrew Ng Intuition: If 1D Solve for (for every ) CSC M.A. Papalaskari - Villanova University 22

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Andrew Ng Size (feet 2 )Number of bedrooms Number of floors Age of home (years) Price ($1000) Size (feet 2 )Number of bedrooms Number of floors Age of home (years) Price ($1000) Examples: CSC M.A. Papalaskari - Villanova University 23

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Andrew Ng Size (feet 2 )Number of bedrooms Number of floors Age of home (years) Price ($1000) Size (feet 2 )Number of bedrooms Number of floors Age of home (years) Price ($1000) Examples: CSC M.A. Papalaskari - Villanova University 24

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Andrew Ng examples ; features. E.g. If CSC M.A. Papalaskari - Villanova University 25

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Andrew Ng is inverse of matrix. Octave: pinv(X’*X)*X’*y CSC M.A. Papalaskari - Villanova University 26

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Andrew Ng training examples, features. Gradient DescentNormal Equation No need to choose. Don’t need to iterate. Need to choose. Needs many iterations. Works well even when is large. Need to compute Slow if is very large. CSC M.A. Papalaskari - Villanova University 27

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CSC M.A. Papalaskari - Villanova University 28 Notes on Supervised learning and Regression Octave Wiki: documentation:

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CSC M.A. Papalaskari - Villanova University 29 Exercise For next class: 1.Download and install Octave (Alternative: if you have MATLAB, you can use it instead.) 2.Verify that it is working by typing in an Octave command window: x = [ ] y = [ ] plot(x,y) This example defines two vectors, x y and should display a plot showing a straight line (the line y=2x). If you get an error at this point, it may be that gnuplot is not installed or cannot access your display. If you are unable to get this to work, you can still do the rest of this exercise, because it does not involve any plotting (just restart Octave). You might refer to the Octave wiki for installation help but if you are stuck, you can get some help troubleshooting this on Friday afternoon 3-4pm in the software engineering lab (mendel 159). 3.Create a few matrices and vectors, eg: A = [1 2; 3 4; 5 6] V = [ ] 4.Try some of the elementary matrix and vector operations from our linear algebra slides (adding, multiplying between matrices, vectors and scalars) 5.Print out a log of your session

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