1 machine learning spring 2010 rong jin. 2 cse847 machine learning instructor: rong jin office hour:...
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Machine Learning
Spring 2010
Rong Jin
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CSE847 Machine Learning Instructor: Rong Jin Office Hour:
Tuesday 4:00pm-5:00pm Thursday 4:00pm-5:00pm
Textbook Machine Learning The Elements of Statistical Learning Pattern Recognition and Machine Learning Many subjects are from papers
Web site: http://www.cse.msu.edu/~cse847
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Requirements ~10 homework assignments Course project
Topic: visual object recognition Data: over one million images with extracted
visual features Objective: build a classifier that automatically
identify the class of objects in images Midterm exam & final exam
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Goal Familiarize you with the state-of-art in
Machine Learning Breadth: many different techniques Depth: Project Hands-on experience
Develop the way of machine learning thinking Learn how to model real-world problems by
machine learning techniques Learn how to deal with practical issues
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Course Outline
Theoretical Aspects• Information Theory
• Optimization Theory
• Probability Theory
• Learning Theory
Practical Aspects• Supervised Learning Algorithms
• Unsupervised Learning Algorithms
• Important Practical Issues
• Applications
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Today’s Topics Why is machine learning? Example: learning to play backgammon General issues in machine learning
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Why Machine Learning? Past: most computer programs are mainly
made by hand Future: Computers should be able to program
themselves by the interaction with their environment
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Recent Trends Recent progress in algorithm and theory Growing flood of online data Computational power is available Growing industry
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Three Niches for Machine Learning Data mining: using historical data to improve
decisions Medical records medical knowledge
Software applications that are difficult to program by hand Autonomous driving Image Classification
User modeling Automatic recommender systems
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Typical Data Mining Task
Given:• 9147 patient records, each describing pregnancy and birth• Each patient contains 215 features
Task:• Classes of future patients at high risk for Emergency Cesarean Section
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Data Mining Results
One of 18 learned rules:If no previous vaginal delivery
abnormal 2nd Trimester Ultrasound Malpresentation at admission
Then probability of Emergency C-Section is 0.6
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Credit Risk Analysis
Learned Rules:If Other-Delinquent-Account > 2
Number-Delinquent-Billing-Cycles > 1Then Profitable-Costumer ? = no
If Other-Delinquent-Account = 0(Income > $30K or Years-of-Credit > 3)
Then Profitable-Costumer ? = yes
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Programs too Difficult to Program By Hand
ALVINN drives 70mph on highways
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Programs too Difficult to Program By Hand
ALVINN drives 70mph on highways
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Programs too Difficult to Program By Hand
Positive Examples
Negative Examples
Sta
tist
ical
Mod
el
Train Test
Classify Bird Images
Visual object recognition
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Image Retrieval using Texts
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Software that Models Users
Description:A homicide detective and a fire marshall must stop a pair of murderers who commit videotaped crimes to become media darlings
Rating:
Description: Benjamin Martin is drawn into the American revolutionary war against his will when a brutal British commander kills his son.
Rating:
Description: A biography of sports legend, Muhammad Ali, from his early days to his days in the ring
Rating:
History What to Recommend?Description: A high-school boy is given the chance to write a story about an up-and-coming rock band as he accompanies it on their concert tour.
Recommend: ?
Description: A young adventurer named Milo Thatch joins an intrepid group of explorers to find the mysterious lost continent of Atlantis.
Recommend: ?
No
Yes
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Netflix Contest
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Relevant Disciplines Artificial Intelligence Statistics (particularly Bayesian Stat.) Computational complexity theory Information theory Optimization theory Philosophy Psychology …
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Today’s Topics Why is machine learning? Example: learning to play backgammon General issues in machine learning
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What is the Learning Problem Learning = Improving with experience at some task
Improve over task T With respect to performance measure P Based on experience E
Example: Learning to Play Backgammon T: Play backgammon P: % of games won in world tournament E: opportunity to play against itself
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Backgammon
More than 1020 states (boards) Best human players see only small fraction of all board
during lifetime Searching is hard because of dice (branching factor > 100)
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TD-Gammon by Tesauro (1995)
Trained by playing with itself Now approximately equal to the best human
player
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Learn to Play Chess Task T: Play chess Performance P: Percent of games won in the
world tournament Experience E:
What experience? How shall it be represented? What exactly should be learned? What specific algorithm to learn it?
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Choose a Target Function Goal:
Policy: : b m Choice of value
function V: b, m
B = board
= real values
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Choose a Target Function Goal:
Policy: : b m Choice of value
function V: b, m V: b
B = board
= real values
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Value Function V(b): Example Definition
If b final board that is won: V(b) = 1 If b final board that is lost: V(b) = -1
If b not final board V(b) = E[V(b*)] where b* is final board after playing optimally
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Representation of Target Function V(b)
Same value
for each board
Lookup table
(one entry for each board)
No Learning No Generalization
Summarize experience into• Polynomials• Neural Networks
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Example: Linear Feature Representation Features:
pb(b), pw(b) = number of black (white) pieces on board b
ub(b), ub(b) = number of unprotected pieces
tb(b), tb(b) = number of pieces threatened by opponent
Linear function: V(b) = w0pb(b)+ w1pw(b)+ w2ub(b)+ w3uw(b)+ w4tb(b)+
w5tw(b)
Learning: Estimation of parameters w0, …, w5
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Given: board b Predicted value V(b) Desired value V*(b)
Calculateerror(b) = (V*(b) – V(b))2
For each board feature fi
wi wi + cerror(b)fi
Stochastically minimizesb (V*(b)-V(b))2
Tuning Weights
Gradient Descent Optimization
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Obtain Boards
Random boards Beginner plays Professionals plays
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Obtain Target Values Person provides value V(b) Play until termination. If outcome is
Win: V(b) 1 for all boards Loss: V(b) -1 for all boards Draw: V(b) 0 for all boards
Play one move: b b’V(b) V(b’)
Play n moves: b b’… b(n)
V(b) V(b(n))
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A General Framework
MathematicalModeling
Finding Optimal Parameters
Statistics Optimization+
Machine Learning
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Today’s Topics Why is machine learning? Example: learning to play backgammon General issues in machine learning
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Importants Issues in Machine Learning Obtaining experience
How to obtain experience? Supervised learning vs. Unsupervised learning
How many examples are enough? PAC learning theory
Learning algorithms What algorithm can approximate function well, when? How does the complexity of learning algorithms impact the learning
accuracy? Whether the target function is learnable?
Representing inputs How to represent the inputs? How to remove the irrelevant information from the input representation? How to reduce the redundancy of the input representation?