Resources 1. Web Page: www.cs.rpi.edu/ magdon/courses/learn.php - - PowerPoint PPT Presentation

Learning From Data Lecture 1 The Learning Problem

Introduction Motivation Credit Default - A Running Example Summary of the Learning Problem

• M. Magdon-Ismail

CSCI 4100/6100

Resources

• 1. Web Page: www.cs.rpi.edu/∼magdon/courses/learn.php

– course info: www.cs.rpi.edu/∼magdon/courses/learn/info.pdf – slides: www.cs.rpi.edu/∼magdon/courses/learn/slides.html – assignments: www.cs.rpi.edu/∼magdon/courses/learn/assign.html

• 2. Text Book:

Learning From Data

Abu-Mostafa, Magdon-Ismail, Lin

• 3. Book Forum: book.caltech.edu/bookforum

– discussion about any material in book including problems and exercises. – additional material

• 4. TA.
• 5. Professor.
• 6. Prerequisites? assignment #0

c A M L Creator: Malik Magdon-Ismail

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The storyline − →

The Storyline

• 1. What is Learning?
• 2. Can We do it?
• 3. How to do it?
• 4. How to do it well?
• 5. General principles?
• 6. Advanced techniques.
• 7. Other Learning Paradigms.

concepts theory practice

• ur language will be mathematics . . .

. . . our sword will be computer algorithms

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The applications − → c A M L Creator: Malik Magdon-Ismail

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Define a tree − →

Let’s Define a Tree?

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A definition − →

Let’s Define a Tree?

A brown trunk moving upwards and branching with leaves . . .

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Does it work? − →

Are These Trees?

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Learning a Tree − →

Learning “What are Trees” is ‘Easy’

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Recognizing is easy − →

Defining is Hard; Recognizing is Easy

Hard to give a complete mathematical definition of a tree. Even a 3 year old can tell a tree from a non-tree. The 3 year old has learned from data.

(Other tasks like graphics or GAN?)

c A M L Creator: Malik Magdon-Ismail

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Rating movies − →

Learning to Rate Movies

• Can we predict how a viewer would rate a movie?
• Why? So that Netflix can make better movie recommendations, and get more rentals.
• $1 million prize for a mere 10% improvement in their recommendation system.

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There’s a pattern, we have data − →

Previous Ratings Reflect Future Ratings

movie: viewer: l i k e s T

• m

C r u i s e ? l i k e s c

• m

e d y ? l i k e s a c t i

• n

? c

• m

e d y c

• n

t e n t T

• m

C r u i s e i n i t ? a c t i

• n

c

• n

t e n t b l

• c

k b u s t e r ?

predicted rating

Match corresponding factors then add their contributions p r e f e r s b l

• c

k b u s t e r s ?

• Viewer taste & movie content imply viewer rating.
• No magical formula to predict viewer rating.
• Netflix has data. We can learn to identify movie

“categories” as well as viewer “preferences”

Class Motto: A pattern exists. We don’t know it. We have data to learn it.

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Credit approval − →

Credit Approval

Let’s use a conceptual example to crystallize the issues.

age 32 years gender male salary 40,000 debt 26,000 years in job 1 year years at home 3 years . . . . . . Approve for credit?

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There’s a pattern, we have data − →

Credit Approval

Let’s use a conceptual example to crystallize the issues.

• Using salary, debt, years in residence, etc., approve for credit or not.
• No magic credit approval formula.
• Banks have lots of data.

– customer information: salary, debt, etc. – whether or not they defaulted on their credit. age 32 years gender male salary 40,000 debt 26,000 years in job 1 year years at home 3 years . . . . . . Approve for credit?

A pattern exists. We don’t know it. We have data to learn it.

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Key players − →

The Key Players

• Salary, debt, years in residence, . . .

input x ∈ Rd = X.

• Approve credit or not
• utput y ∈ {−1, +1} = Y.
• True relationship between x and y

target function f : X → Y.

(The target f is unknown.)

• Data on customers

data set D = (x1, y1), . . . , (xN, yN).

(yn = f(xn).)

X Y and D are given by the learning problem; The target f is fixed but unknown.

We learn the function f from the data D.

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Learning − →

Learning

• Start with a set of candidate hypotheses H which you think are likely to represent f.

H = {h1, h2, . . . , } is called the hypothesis set or model.

• Select a hypothesis g from H. The way we do this is called a learning algorithm.
• Use g for new customers. We hope g ≈ f.

X Y and D are given by the learning problem; The target f is fixed but unknown. We choose H and the learning algorithm

This is a very general setup (eg. choose H to be all possible hypotheses)

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Summary of learning setup − →

Summary of the Learning Setup

(ideal credit approval formula) (historical records of credit customers) (set of candidate formulas) (learned credit approval formula) UNKNOWN TARGET FUNCTION f : X → Y TRAINING EXAMPLES (x1, y1), (x2, y2), . . . , (xN, yN) HYPOTHESIS SET H FINAL HYPOTHESIS g ≈ f LEARNING ALGORITHM A

yn = f(xn)

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