01_introduction_ML.pdf

CIS 419/519
Introduction to
Machine Learning
Instructor: Eric Eaton
www.seas.upenn.edu/~cis519
1
Robot Image Credit: Viktoriya Sukhanova © 123RF.com
These slides were assembled by Eric Eaton, with grateful acknowledgement of the many others who made
their course materials freely available online. Feel free to reuse or adapt these slides for your own academic
purposes, provided that you include proper attribution. Please send comments and corrections to Eric.

What is Machine Learning?
“Learning is any process by which a system improves
performance from experience.”
- Herbert Simon
Definition by Tom Mitchell (1998):
Machine Learning is the study of algorithms that
• improve their performance P
• at some task T
• with experience E.
A well-defined learning task is given by <P, T, E>.
3

Traditional Programming
Machine Learning
Computer
Data
Program
Output
Computer
Data
Output
Program
Slide credit: Pedro Domingos
4

When Do We Use Machine Learning?
ML is used when:
• Human expertise does not exist (navigating on Mars)
• Humans can’t explain their expertise (speech recognition)
• Models must be customized (personalized medicine)
• Models are based on huge amounts of data (genomics)
Learning isn’t always useful:
• There is no need to “learn” to calculate payroll
Based on slide by E. Alpaydin
5

A classic example of a task that requires machine learning:
It is very hard to say what makes a 2
Slide credit: Geoffrey Hinton
6

Some more examples of tasks that are best
solved by using a learning algorithm
• Recognizing patterns:
– Facial identities or facial expressions
– Handwritten or spoken words
– Medical images
• Generating patterns:
– Generating images or motion sequences
• Recognizing anomalies:
– Unusual credit card transactions
– Unusual patterns of sensor readings in a nuclear power plant
• Prediction:
– Future stock prices or currency exchange rates
Slide credit: Geoffrey Hinton
7

Sample Applications
• Web search
• Computational biology
• Finance
• E-commerce
• Space exploration
• Robotics
• Information extraction
• Social networks
• Debugging software
• [Your favorite area]
8

Samuel’s Checkers-Player
“Machine Learning: Field of study that gives
computers the ability to learn without being
explicitly programmed.” -Arthur Samuel (1959)
9

Defining the Learning Task
Improve on task T, with respect to
performance metric P, based on experience E
T: Playing checkers
P: Percentage of games won against an arbitrary opponent
E: Playing practice games against itself
T: Recognizing hand-written words
P: Percentage of words correctly classified
E: Database of human-labeled images of handwritten words
T: Driving on four-lane highways using vision sensors
P: Average distance traveled before a human-judged error
E: A sequence of images and steering commands recorded while
observing a human driver.
T: Categorize email messages as spam or legitimate.
P: Percentage of email messages correctly classified.
E: Database of emails, some with human-given labels
Slide credit: Ray Mooney
10

State of the Art Applications of
Machine Learning
11

Autonomous Cars
Penn’s Autonomous Car à
(Ben Franklin Racing Team)
• Nevada made it legal for
autonomous cars to drive on
roads in June 2011
• As of 2013, four states (Nevada,
Florida, California, and
Michigan) have legalized
autonomous cars
12

Autonomous Car Technology
Laser Terrain Mapping
Sebastian
Stanley
Adaptive Vision
Learning from Human Drivers
Path
Planning
Images and movies taken from Sebastian Thrun’s multimedia website.
14

Deep Learning in the Headlines
15

pixels
edges
object parts
(combination
of edges)
object models
Deep Belief Net on Face Images
Based on materials
by Andrew Ng
16

Examples of learned object parts from object categories
Learning of Object Parts
Faces Cars Elephants Chairs
Slide credit: Andrew Ng
17

Training on Multiple Objects
Trained on 4 classes (cars, faces,
motorbikes, airplanes).
Second layer: Shared-features
and object-specific features.
Third layer: More specific
features.
18

Scene Labeling via Deep Learning
[Farabet et al. ICML 2012, PAMI 2013] 19

Input images
Samples from
feedforward
Inference
(control)
Samples from
Full posterior
inference
Inference from Deep Learned Models
Generating posterior samples from faces by “filling in” experiments
(cf. Lee and Mumford, 2003). Combine bottom-up and top-down inference.
20

Machine Learning in
Automatic Speech Recognition
A Typical Speech Recognition System
ML used to predict of phone states from the sound spectrogram
Deep learning has state-of-the-art results
# Hidden Layers 1 2 4 8 10 12
Word Error Rate % 16.0 12.8 11.4 10.9 11.0 11.1
Baseline GMM performance = 15.4%
[Zeiler et al. “On rectified linear units for speech
recognition” ICASSP 2013]
21

Impact of Deep Learning in Speech Technology
Slide credit: Li Deng, MS Research
22

Types of Learning
• Supervised (inductive) learning
– Given: training data + desired outputs (labels)
• Unsupervised learning
– Given: training data (without desired outputs)
• Semi-supervised learning
– Given: training data + a few desired outputs
• Reinforcement learning
– Rewards from sequence of actions
Based on slide by Pedro Domingos
24

Supervised Learning: Regression
• Given (x1, y1), (x2, y2), ..., (xn, yn)
• Learn a function f(x) to predict y given x
– y is real-valued == regression
0
1
2
3
4
5
6
7
8
9
1970 1980 1990 2000 2010 2020
September
Arctic
Sea
Ice
Extent
(1,000,000
sq
km)
Year
Data from G. Witt. Journal of Statistics Education, Volume 21, Number 1 (2013)
26

Supervised Learning: Classification
• Given (x1, y1), (x2, y2), ..., (xn, yn)
– y is categorical == classification
1(Malignant)
0(Benign)
Tumor Size
Breast Cancer (Malignant / Benign)
Based on example by Andrew Ng
27

• Given (x1, y1), (x2, y2), ..., (xn, yn)
1(Malignant)
0(Benign)
Tumor Size
Tumor Size
28

• Given (x1, y1), (x2, y2), ..., (xn, yn)
1(Malignant)
0(Benign)
Tumor Size
Tumor Size
Predict Malignant
Predict Benign
29

Supervised Learning
Tumor Size
Age
- Clump Thickness
- Uniformity of Cell Size
- Uniformity of Cell Shape
…
• x can be multi-dimensional
– Each dimension corresponds to an attribute
30

Unsupervised Learning
• Given x1, x2, ..., xn (without labels)
• Output hidden structure behind the x’s
– E.g., clustering
31

[Source: Daphne Koller]
Genes
Individuals
Genomics application: group individuals by genetic similarity
32

Organize computing clusters Social network analysis
Image credit: NASA/JPL-Caltech/E. Churchwell (Univ. of Wisconsin, Madison)
Astronomical data analysis
Market segmentation
33

• Independent component analysis – separate a
combined signal into its original sources
Image credit: statsoft.com Audio from http://www.ism.ac.jp/~shiro/research/blindsep.html
34

• Independent component analysis – separate a
combined signal into its original sources
Image credit: statsoft.com Audio from http://www.ism.ac.jp/~shiro/research/blindsep.html
35

Reinforcement Learning
• Given a sequence of states and actions with
(delayed) rewards, output a policy
– Policy is a mapping from states à actions that
tells you what to do in a given state
• Examples:
– Credit assignment problem
– Game playing
– Robot in a maze
– Balance a pole on your hand
36

The Agent-Environment Interface
Agent and environment interact at discrete time steps : t = 0, 1, 2, K
Agent observes state at step t: st ∈S
produces action at step t : at ∈ A(st )
gets resulting reward : rt +1 ∈ℜ
and resulting next state : st +1
t
. . . st a
rt +1 st +1
t +1
a
rt +2 st +2
t +2
a
rt +3 st +3
. . .
t +3
a
Slide credit: Sutton & Barto
37

Reinforcement Learning
https://www.youtube.com/watch?v=4cgWya-wjgY 38

Inverse Reinforcement Learning
• Learn policy from user demonstrations
Stanford Autonomous Helicopter
http://heli.stanford.edu/
https://www.youtube.com/watch?v=VCdxqn0fcnE
39

Designing a Learning System
• Choose the training experience
• Choose exactly what is to be learned
– i.e. the target function
• Choose how to represent the target function
• Choose a learning algorithm to infer the target
function from the experience
Environment/
Experience
Learner
Knowledge
Performance
Element
Based on slide by Ray Mooney
Training data
Testing data
41

Training vs. Test Distribution
• We generally assume that the training and
test examples are independently drawn from
the same overall distribution of data
– We call this “i.i.d” which stands for “independent
and identically distributed”
• If examples are not independent, requires
collective classification
• If test distribution is different, requires
transfer learning
42

ML in a Nutshell
• Tens of thousands of machine learning
algorithms
– Hundreds new every year
• Every ML algorithm has three components:
– Representation
– Optimization
– Evaluation
43

Various Function Representations
• Numerical functions
– Linear regression
– Neural networks
– Support vector machines
• Symbolic functions
– Decision trees
– Rules in propositional logic
– Rules in first-order predicate logic
• Instance-based functions
– Nearest-neighbor
– Case-based
• Probabilistic Graphical Models
– Naïve Bayes
– Bayesian networks
– Hidden-Markov Models (HMMs)
– Probabilistic Context Free Grammars (PCFGs)
– Markov networks
44

Various Search/Optimization
Algorithms
• Gradient descent
– Perceptron
– Backpropagation
• Dynamic Programming
– HMM Learning
– PCFG Learning
• Divide and Conquer
– Decision tree induction
– Rule learning
• Evolutionary Computation
– Genetic Algorithms (GAs)
– Genetic Programming (GP)
– Neuro-evolution
45

Evaluation
• Accuracy
• Precision and recall
• Squared error
• Likelihood
• Posterior probability
• Cost / Utility
• Margin
• Entropy
• K-L divergence
• etc.
47

ML in Practice
• Understand domain, prior knowledge, and goals
• Data integration, selection, cleaning, pre-processing, etc.
• Learn models
• Interpret results
• Consolidate and deploy discovered knowledge
Based on a slide by Pedro Domingos
Loop
48

49
Lessons Learned about Learning
• Learning can be viewed as using direct or indirect
experience to approximate a chosen target function.
• Function approximation can be viewed as a search
through a space of hypotheses (representations of
functions) for one that best fits a set of training data.
• Different learning methods assume different
hypothesis spaces (representation languages) and/or
employ different search techniques.

A Brief History of
Machine Learning
50

History of Machine Learning
• 1950s
– Samuel’s checker player
– Selfridge’s Pandemonium
• 1960s:
– Neural networks: Perceptron
– Pattern recognition
– Learning in the limit theory
– Minsky and Papert prove limitations of Perceptron
• 1970s:
– Symbolic concept induction
– Winston’s arch learner
– Expert systems and the knowledge acquisition bottleneck
– Quinlan’s ID3
– Michalski’s AQ and soybean diagnosis
– Scientific discovery with BACON
– Mathematical discovery with AM
51

History of Machine Learning (cont.)
• 1980s:
– Advanced decision tree and rule learning
– Explanation-based Learning (EBL)
– Learning and planning and problem solving
– Utility problem
– Analogy
– Cognitive architectures
– Resurgence of neural networks (connectionism, backpropagation)
– Valiant’s PAC Learning Theory
– Focus on experimental methodology
• 1990s
– Data mining
– Adaptive software agents and web applications
– Text learning
– Reinforcement learning (RL)
– Inductive Logic Programming (ILP)
– Ensembles: Bagging, Boosting, and Stacking
– Bayes Net learning
52

History of Machine Learning (cont.)
• 2000s
– Support vector machines & kernel methods
– Graphical models
– Statistical relational learning
– Transfer learning
– Sequence labeling
– Collective classification and structured outputs
– Computer Systems Applications (Compilers, Debugging, Graphics, Security)
– E-mail management
– Personalized assistants that learn
– Learning in robotics and vision
• 2010s
– Deep learning systems
– Learning for big data
– Bayesian methods
– Multi-task & lifelong learning
– Applications to vision, speech, social networks, learning to read, etc.
– ???
Based on slide by Ray Mooney
53

What We’ll Cover in this Course
• Supervised learning
– Decision tree induction
– Linear regression
– Logistic regression
– Support vector machines
& kernel methods
– Model ensembles
– Bayesian learning
– Neural networks & deep
learning
– Learning theory
• Unsupervised learning
– Clustering
– Dimensionality reduction
• Reinforcement learning
– Temporal difference
learning
– Q learning
• Evaluation
• Applications
Our focus will be on applying machine learning to real applications
54

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