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CS760 – Machine Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Textbooks & Reading Assignment ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Monday, Wednesday, and Friday? ,[object Object],[object Object],[object Object]
Course "Style" ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
"MS vs. PhD" Aspects ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Two Major Goals ,[object Object],[object Object],[object Object],[object Object]
Background Assumed ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Requirements ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Grading ,[object Object],[object Object],[object Object],[object Object]
Late HW's Policy ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Academic Misconduct (also on course homepage) ,[object Object]
What Do You Think Learning Means?
What is Learning? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Today’s Topics ,[object Object],[object Object],[object Object],[object Object]
Memorization (Rote Learning) ,[object Object],[object Object],[object Object],[object Object]
Rote Learning is Limited ,[object Object],[object Object],[object Object]
Some Settings in Which Learning May Help ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Broad Paradigms of Machine Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Not in Mitchell’s textbook (covered in CS 776)
IID (Completion of Lec #2) ,[object Object],[object Object],[object Object]
Supervised Learning Task Overview Concepts/ Classes/ Decisions Feature Selection (usually done by humans) Classification Rule Construction (done by learning algorithm) Real World Feature Space HW 0 HW 1-3
Supervised Learning Task Overview (cont.) ,[object Object],[object Object],[object Object]
Empirical Learning: Task Definition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],The Key Point!
Example Positive Examples Negative Examples How does this symbol classify? ,[object Object],[object Object],[object Object],[object Object],[object Object]
Concept Learning ,[object Object],. . . Training Examples Backpropagation C4.5, CART AQ, FOIL SVMs Neural Net Decision Tree Φ <- X^Y Φ <- Z Rules If 5x 1 + 9x 2 – 3x 3 > 12 Then +
Feature Space ,[object Object],Size Color Weight ? Big 2500 Gray A “concept” is then a (possibly disjoint) volume in this space.
Learning from Labeled Examples ,[object Object],Venn Diagram + + + + - - - - - - - - ,[object Object],[object Object],[object Object]
Brief Review ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],A A A “ and” “ or” Instances
Empirical Learning and Venn Diagrams ,[object Object],[object Object],[object Object],Venn Diagram A B - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + Feature Space
Aspects of an ML System ,[object Object],[object Object],[object Object],[object Object],[object Object],HW 0 Other HW’s
Nearest-Neighbor Algorithms ,[object Object],[object Object],[object Object],Venn - - - - - - - - + + + + + + + + + + ? … “ Voronoi Diagrams” (pg 233)
Simple Example: 1-NN ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],So output - (1-NN ≡ one nearest neighbor)
Sample Experimental Results (see UCI archive for more) Simple algorithm works quite well! Testset Correctness Testbed 86% 85% 83% Appendicitis ? 38% 37% Tumor ? 76% 78% Heart Disease 96% 95% 98% Wisconsin Cancer Neural Nets D-Trees 1-NN
K -NN Algorithm ,[object Object],[object Object],[object Object],[object Object],1 Shouldn’t really “ connect the dots” (Why?) Tuning Set Error Rate 2 3 4 5 K
Data Representation ,[object Object],[object Object],[object Object],[object Object],fixed length feature vectors
HW0 – Create Your Own Dataset (repeated from lecture #1) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
HW0 – Your “Personal Concept” ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Some Real-World Examples ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Medical record Learned Function Steering Angle Digitized camera image age=13, sex=M, wgt=18 Learned Function sick vs healthy
HW0 – Your “Personal Concept” ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Defines a space In HW0 we will use a subset (see next slide)
Standard Feature Types for representing training examples – a source of “ domain knowledge ” ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],closed polygon continuous triangle square circle ellipse
Our Feature Types (for CS 760 HW’s) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Example Hierarchy (KDD* Journal, Vol 5, No. 1-2, 2001, page 17) ,[object Object],[object Object],[object Object],* Officially, “Data Mining and Knowledge Discovery”, Kluwer Publishers Product Pct Foods Tea Canned Cat Food Dried Cat Food 99 Product Classes 2302 Product Subclasses Friskies Liver, 250g ~30k Products
HW0: Creating Your Dataset ,[object Object],Studio Movie Director/ Producer Actor Made Acted in Directed Name Country List of movies Name Year of birth Gender Oscar nominations List of movies Title, Genre, Year, Opening Wkend BO receipts , List of actors/actresses, Release season Name Year of birth List of movies Produced
HW0: Sample DB ,[object Object],[object Object],[object Object],[object Object]
HW0: Representing as a Fixed-Length Feature Vector ,[object Object],[object Object]
[email_address] ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
First Algorithm in Detail ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Some Common Jargon ,[object Object],[object Object],[object Object],[object Object],[object Object],Discrete/Real Outputs
Variations on a Theme ,[object Object],[object Object],[object Object],[object Object],[object Object],(From Aha, Kibler and Albert in ML Journal)
Variations on a Theme (cont.) ,[object Object],[object Object],[object Object],[object Object]
Distance Functions ,[object Object],[object Object],[object Object]
Distance Functions (sample) distance between examples 1 and 2 a numeric weighting factor distance for feature i only between examples 1 and 2
Kernel Functions and k -NN ,[object Object],[object Object],[object Object]
Kernel Functions and k -NN (continued) ,[object Object],[object Object],[object Object],[object Object],the kernel “ delta” function (=1 if O i =c , else =0)
Sample Kernel Functions  (e i , e t ) ,[object Object],[object Object],simple majority vote (? classified as -) inverse distance weight (? could be classified as +) In diagram to right, example ‘?’ has three neighbors, two of which are ‘-’ and one of which is ‘+’. - - + ?
Gaussian Kernel ,[object Object],Euler’s constant
Local Learning ,[object Object],[object Object],[object Object],+ + + + + + + - - - - ? - Train on these
Instance-Based Learning (IBL) and Efficiency ,[object Object],[object Object],[object Object],[object Object],[object Object]
Instance-Based Learning (IBL) and Efficiency ,[object Object],[object Object],[object Object],[object Object],[object Object]
Number of Features and Performance ,[object Object],[object Object],[object Object]
Feature Selection and ML (general issue for ML) ,[object Object],[object Object],[object Object],[object Object],[object Object],FS algorithm ML algorithm ML algorithm all features model FS algorithm calls ML algorithm many times, uses it to help select features
Feature Selection as Search Problem ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Forward and Backward Selection of Features ,[object Object],Forward Backward add F 1 ... ... Features to use Accuracy on tuning set (our heuristic function) ... ... {} 50% {F N } 71% {F 1 } 62% add F N add F 1 {F 1 ,F 2 ,...,F N } 73% {F 2 ,...,F N } 79% subtract F 1 subtract F 2
Forward vs. Backward Feature Selection ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Forward Backward
Some Comments on k -NN ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Positive Negative
Questions about IBL (Breiman et al. - CART book) ,[object Object],[object Object],[object Object]
Questions about IBL (Breiman et al. - CART book) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
More IBL Criticisms ,[object Object],[object Object],[object Object],[object Object],[object Object]
Summary ,[object Object],[object Object]

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MLlecture1.ppt

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  • 12. What Do You Think Learning Means?
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  • 20. Supervised Learning Task Overview Concepts/ Classes/ Decisions Feature Selection (usually done by humans) Classification Rule Construction (done by learning algorithm) Real World Feature Space HW 0 HW 1-3
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  • 32. Sample Experimental Results (see UCI archive for more) Simple algorithm works quite well! Testset Correctness Testbed 86% 85% 83% Appendicitis ? 38% 37% Tumor ? 76% 78% Heart Disease 96% 95% 98% Wisconsin Cancer Neural Nets D-Trees 1-NN
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  • 51. Distance Functions (sample) distance between examples 1 and 2 a numeric weighting factor distance for feature i only between examples 1 and 2
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