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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

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

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

Course Outline

- Theoretical Aspects
- Information Theory
- Optimization Theory
- Probability Theory
- Learning Theory

- Practical Aspects
- Supervised Learning Algorithms
- Unsupervised Learning Algorithms
- Important Practical Issues
- Applications

Today’s Topics

- Why is machine learning?
- Example: learning to play backgammon
- General issues in machine learning

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

Recent Trends

- Recent progress in algorithm and theory
- Growing flood of online data
- Computational power is available
- Growing industry

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

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

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

Credit Risk Analysis

Learned Rules:

If Other-Delinquent-Account > 2

Number-Delinquent-Billing-Cycles > 1

Then Profitable-Costumer ? = no

If Other-Delinquent-Account = 0

(Income > $30K or Years-of-Credit > 3)

Then Profitable-Costumer ? = yes

Programs too Difficult to Program By Hand

- ALVINN drives 70mph on highways

Programs too Difficult to Program By Hand

- ALVINN drives 70mph on highways

Programs too Difficult to Program By Hand

- Visual object recognition

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: ?

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Rating:

Description: A biography of sports legend, Muhammad Ali, from his early days to his days in the ring

Rating:

No

Description: A young adventurer named Milo Thatch joins an intrepid group of explorers to find the mysterious lost continent of Atlantis.

Recommend: ?

Description:Benjamin Martin is drawn into the American revolutionary war against his will when a brutal British commander kills his son.

Rating:

Yes

Software that Models UsersHistory

Relevant Disciplines

- Artificial Intelligence
- Statistics (particularly Bayesian Stat.)
- Computational complexity theory
- Information theory
- Optimization theory
- Philosophy
- Psychology
- …

Today’s Topics

- Why is machine learning?
- Example: learning to play backgammon
- General issues in machine learning

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

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)

TD-Gammon by Tesauro (1995)

- Trained by playing with itself
- Now approximately equal to the best human player

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?

Choose a Target Function

- Goal:
- Policy: : b m

- Choice of value function
- V: b, m

B = board

= real values

Choose a Target Function

- Goal:
- Policy: : b m

- Choice of value function
- V: b, m
- V: b

B = board

= real values

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

Representation of Target Function V(b)

Same value

for each board

Lookup table

(one entry for each board)

- Summarize experience into
- Polynomials
- Neural Networks

No Learning

No Generalization

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

Tuning Weights

- Given:
- board b
- Predicted value V(b)
- Desired value V*(b)

- Calculate
error(b) = (V*(b) – V(b))2

For each board feature fi

wi wi + cerror(b)fi

- Stochastically minimizes
b (V*(b)-V(b))2

Gradient Descent Optimization

Obtain Boards

- Random boards
- Beginner plays
- Professionals plays

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))

Finding Optimal Parameters

+

Statistics

Optimization

Machine Learning

A General FrameworkToday’s Topics

- Why is machine learning?
- Example: learning to play backgammon
- General issues in machine learning

Importants Issues in Machine Learning

- Obtaining experience
- How to obtain experience?
- Supervised learning vs. Unsupervised learning

- How many examples are enough?
- PAC learning theory

- How to obtain experience?
- 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?

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