csc 4510 – machine learning dr. mary-angela papalaskari department of computing sciences villanova...

CSC 4510 – Machine LearningDr. Mary-Angela PapalaskariDepartment of Computing SciencesVillanova University

Course website: www.csc.villanova.edu/~map/4510/

5: Multivariate Regression

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The slides in this presentation are adapted from:• Andrew Ng’s ML course http://www.ml-class.org/

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

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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

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Andrew Ng

Size (feet2) Number of bedrooms

Number of floors

Age of home (years)

Price ($1000)

2104 5 1 45 4601416 3 2 40 2321534 3 2 30 315852 2 1 36 178… … … … …

Multiple features (variables).

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Andrew Ng

Size (feet2) Number of bedrooms

Number of floors

Age of home (years)

Price ($1000)

2104 5 1 45 4601416 3 2 40 2321534 3 2 30 315852 2 1 36 178… … … … …

Multiple features (variables).

Notation:= number of features= input (features) of training example.

= value of feature in training example.CSC 4510 - M.A. Papalaskari - Villanova

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Andrew Ng

Size (feet2) Price ($1000)

2104 4601416 2321534 315852 178… …

Multiple features (variables).

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For convenience of notation, define .

Multivariate linear regression

Hypothesis:Previously:

Now:

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Hypothesis:

Cost function:

Parameters:

(simultaneously update for every )

Repeat

Gradient descent:

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(simultaneously update )

Gradient Descent

Repeat

Previously (n=1):

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(simultaneously update )

Gradient Descent

Repeat

Previously (n=1):

New algorithm :Repeat

(simultaneously update for )

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(simultaneously update )

Gradient Descent

Repeat

Previously (n=1):

New algorithm :Repeat

(simultaneously update for )

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E.g. = size (0-2000 feet2)

= number of bedrooms (1-5)

Feature ScalingIdea: Make sure features are on a similar scale.

size (feet2)

number of bedrooms

Get every feature into range

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E.g. = size (0-2000 feet2)

= number of bedrooms (1-5)

Feature ScalingIdea: 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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Gradient descent

- “Debugging”: How to make sure gradient descent is working correctly.

- How to choose learning rate .

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0 100 200 300 400

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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Summary: Choosing - If is too small: slow convergence.- If is too large: may not decrease on

every iteration; may not converge.

To choose , try

Andrew Ng

Housing prices prediction

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Andrew Ng

Polynomial regression

Price(y)

Size (x)

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Andrew Ng

Choice of features

Price(y)

Size (x)

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Andrew Ng

Gradient Descent

Normal equation: Method to solve for analytically.

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Andrew Ng

Intuition: If 1D

Solve for

(for every )

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Andrew Ng

Size (feet2) Number of bedrooms

Number of floors

Age of home (years)

Price ($1000)

1 2104 5 1 45 4601 1416 3 2 40 2321 1534 3 2 30 3151 852 2 1 36 178

Size (feet2) Number of bedrooms

Number of floors

Age of home (years)

Price ($1000)

2104 5 1 45 4601416 3 2 40 2321534 3 2 30 315852 2 1 36 178

Examples:

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Andrew Ng

examples ; features.

E.g. If

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Andrew Ng

is inverse of matrix .

Octave: pinv(X’*X)*X’*y

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Andrew Ng

training examples, features.Gradient Descent Normal 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.

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Notes on Supervised learning and Regression

http://see.stanford.edu/materials/aimlcs229/cs229-notes1.pdf

Octave http://www.gnu.org/software/octave/

Wiki: http://www.octave.org/wiki/index.php?title=Main_Page

documentation:http://www.gnu.org/software/octave/doc/interpreter/

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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 = [0 1 2 3]y = [0 2 4 6]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 = [3 5 -1 0 7]

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